Job Title: Machine Learning Engineer Company: Zurich Canada Job Category: Machine Learning Roles & Responsibilities: Are you looking for a caring, collaborative, values\-driven workplace with inspiring teammates and leaders? Do you have the ambition and desire to be the best and thrive at the most impactful global insurance provider in the world? Look no further than Zurich Canada. Zurich Canada is expanding its AI Engineering capability and is looking for Machine Learning / AI Engineers to design, develop and operationalize scalable AI and machine learning solutions. What you will do Machine Learning Engineer \- Applied AI * Build/productionize predictive ML/AI: classification, regression, forecasting and clustering. * Build feature pipelines; establish training, validation/testing and bias/fairness testing. * Evaluate models; move experiments to production and optimize inference. * Assess approved A/B, shadow or controlled pilots; monitor data quality, performance/drift and troubleshoot production issues. * Document assumptions, limitations, versions, training\-data references and approvals. Machine Learning Engineer \- Generative AI * Engineer, evaluate and productionize enterprise GenAI using foundation models, retrieval and agentic patterns. * Build LLM services and RAG pipelines covering ingestion, parsing, cleaning, chunking, embeddings, indexing/retrieval. * Evaluate keyword, semantic\-vector/hybrid search, filtering/reranking; design prompts, structured outputs/context management. * Improve groundedness through retrieval quality, grounding checks, citations, abstention and human review. * Build controlled agentic workflows/tool integrations; evaluate retrieval, answers, safety, agent behaviour/business outcomes. * Monitor quality, groundedness, safety, latency, reliability/cost; apply access\-aware retrieval, sensitive\-data controls and prompt\-injection defences. * Maintain auditable model, prompt, data, index and evaluation records. Machine Learning Engineer \- Data \& ML Platforms * Build/operate enterprise\-scale data, feature and engineering foundations for ML/AI. * Develop training and batch/real\-time inference pipelines, reusable feature frameworks and batch/streaming components. * Optimize Spark/distributed processing; build secure data\-platform/AI interfaces and reusable libraries/templates. * Implement ML dataset quality monitoring; support feature management, lineage/reproducibility. * Engineer large\-scale structured/unstructured data; deploy, monitor and troubleshoot production ML data pipelines. MLOps Engineer * Build/operate platforms, automation and controls across the ML/AI lifecycle. * Design CI/CD for models/AI applications; establish model registration, versioning, lineage/promotion. * Build reproducible training/deployment environments; support staged releases, shadow deployments/rollback. * Monitor health, latency, data quality, model performance/drift; develop retraining/redeployment workflows with validation/approvals. * Manage ML infrastructure/configuration; implement access controls, audit trails/governance evidence and reusable MLOps standards. * Python and SQL skills. * Software engineering fundamentals including Git, APIs, testing and CI/CD. * Understanding of ML concepts and the model\-development lifecycle. * Experience with data processing and analytical workloads. * Experience in cloud\-based development environments. * Understanding of reliable, maintainable, production\-ready solutions. * Databricks, MLflow and Apache Spark/PySpark. * Microsoft Azure or equivalent AWS/Google Cloud technologies. * Azure DevOps or GitHub. * Docker and containerized applications. * REST APIs. * scikit\-learn, PyTorch, TensorFlow or comparable ML/deep\-learning frameworks. * Azure OpenAI or comparable managed AI platforms. * RAG, embeddings, retrieval and LLM evaluation. * Infrastructure as code, Kubernetes and production observability. * Security, privacy, Responsible AI and model governance in a regulated environment. Intermediate * Independently design/deliver ML/AI components and solutions. * Own development through production implementation. * Make appropriate engineering and technology choices. * Troubleshoot complex data, model and production issues. * Contribute to architecture and engineering standards. * Mentor junior team members. * Work with stakeholders to refine technical requirements. Senior * Lead design/engineering of complex enterprise AI/ML solutions. * Make architecture and technology decisions. * Establish reusable patterns and engineering standards. * Lead production\-readiness, scalability, reliability and security decisions. * Mentor engineers and provide technical leadership. * Drive engineering excellence through design/code reviews. * Partner with architecture, security, governance and business leadership. * Evaluate emerging AI technologies and appropriate enterprise adoption patterns. We value diverse perspectives and foster a collaborative environment where employees are empowered to make an impact. We offer a comprehensive rewards package designed to support employee well\-being, growth, and success. * Comprehensive health and wellness benefits. * Minimum four weeks of vacation annually. * Four personal days each year to support personal, cultural, religious, or well\-being needs. * Access to ongoing learning and development opportunities. * Opportunities to support and give back to our communities. * Recognition as one of Greater Toronto's Top Employers and recipient of Insurance Business Canada's 5\-Star Diversity, Equity and Inclusion Award. Make a difference. Be challenged. Be inspired. Be supported. Love what you do. Work for us. A future with Zurich Now is the time to move forward and make a difference. At Zurich, we want you to share your unique perspectives, experiences and ideas so we can grow and drive sustainable change together. As part of a leading global organization, Zurich North America has 0 years of experience managing risk and supporting resilience. We are a leading provider of commercial property\-casualty insurance solutions and a wide range of risk management products and services for businesses and individuals. Today, we serve more than 25 industries, from agriculture to technology and insure 90% of the Fortune 500®. Our growth strategy is not limited to our business. As an employer, Zurich strives to provide ongoing career development opportunities and foster an environment where voices are diverse, behaviors are inclusive, actions drive equity, and our people feel a sense of belonging. Be a part of the next evolution of the insurance industry. Join us in building a brighter future for our people, our customers and the communities we serve. Only applicable for Canada: For Zurich Canada's commitment to diversity and accessibility please click here. If you would prefer to not receive future electronic messages from Zurich Insurance Company Ltd's recruitment management system, please email your request to have your job profile deleted from the system by clicking here. Zurich Insurance Company Ltd 100 King St., W., Suite 5500, Toronto, ON, M5X 1C9\. www.zurichcanada.com Work Location: Waterloo, Ontario, Canada Availability: Full time

Job Title: Senior Machine Learning Engineer - Content and Contributor Intelligence (Remote - CAN) Company: Yelp Job Category: Machine Learning Roles & Responsibilities: Yelp engineering culture is driven by our values: we’re a cooperative team that values individual authenticity and encourages creative solutions to problems. All new engineers deploy working code their first week, and we strive to broaden individual impact with support from managers, mentors, and teams. At the end of the day, we’re all about helping our users, growing as engineers, and having fun in a collaborative environment. * Conduct end\-to\-end analyses, wrangling data via SQL or Python, to statistical modeling, to hypothesizing and presenting business ideas. * Mentor and guide junior engineers, fostering a culture of learning and technical excellence. * Work with large and complex textual and visual datasets. * Support the development and deployment of projects involving machine learned models for offline, batch\-based data products as well as models deployed to online, real\-time services. * Work in the contributor and visual intelligence team on text and visual understanding, along with fine tuning transformer models to derive embeddings for multiple input types * Productionize and automate model pipelines within Python services. * Drive and advocate adoption of best practices in ML development and operations, and mentor newer engineers in those practices. * Experience developing and productionizing machine learning models, particularly in neural networks, computer vision and LLMs including their supported data pipelines. * Experience with machine learning using packages such as PyTorch, TensorFlow, Spark MLlib, XGBoost, and Sklearn. * Strong coding skills in Python or equivalent (Java, C\+\+). * Solid understanding of engineering and infrastructure best practices. * The curiosity to uncover promising solutions to new problems, and the persistence to carry your ideas through to an end goal. * We highly value experience of working with LLMs, utilizing LLM APIs (OpenAI, Bedrock, etc), prompt engineering and evaluation. Work Location: Vancouver, Canada Availability: Full time

Job Title: Staff DevOps Engineer Company: Terawatt Infrastructure Job Category: Machine Learning Roles & Responsibilities: The once in a century transition to autonomous and electric vehicles is underway and will require a multi\-trillion\-dollar investment in energy and charging infrastructure, and the real estate to site it on. Terawatt is the leader in delivering large scale, turnkey charging solutions for companies rapidly deploying AV and EV fleets. Whether it’s an urban mobility hub, or a carefully located multi\-fleet hub for semi\-trucks, Terawatt brings the talent, capabilities, and capital to create reliable, cost\-effective solutions for customers on the leading edge of the transition to the next generation of transport. With a growing portfolio of sites across the US in urban hubs and along key logistics and transportation corridors. Terawatt is building the permanent transportation and logistics infrastructure of tomorrow through a robust combination of capital, real estate, development, and site operations solutions. The company develops, finances, owns, and operates charging solutions that take the cost and complexity out of electrifying fleets. Terawatt is not a traditional infrastructure operator. Our technology platform transforms a portfolio of sites into a reliable, intelligent network — through advanced power management, operational optimization, and data\-driven systems that back our customers with hard performance guarantees. That's what makes us a de\-risked operator and a category\-defining company. ### Role Description Terawatt's headquarters is located in San Francisco, CA; this is a remote role where candidates must live in the Greater Toronto, ON Area.* Join Terawatt’s innovative technology team as a Staff DevOps Engineer and help drive the evolution of our platform to support one of the leading fleet EV charging solutions in North America. In this role, you will lead the development and reliability of Terawatt’s charging network management system and the capabilities that enable a high\-quality site experience for customers. Working closely with the SVP of Technology, the Product team, and other technical partners, you will play a key role in expanding our platform and delivering meaningful value in a rapidly evolving industry. This role will help scale our cloud infrastructure and support organizational growth by implementing best practices that improve the reliability, performance, and maintainability of our charging infrastructure and software systems. You will also own the infrastructure behind Terawatt's internal tools and AI platform, partnering with our AI Product Manager and engineers to deploy, secure, observe, and scale agentic workflows across the company. ### Core Responsibilities * Set technical direction for DevOps and platform engineering, mentor engineers, and champion infrastructure standards across the organization. * Lead planning for team goals that align with overall business goals and technology roadmap * Lead and architect the evolution of our cloud infrastructure using Terraform, building resilient and scalable systems to support business growth. * Maintain helm charts and deployment patterns that enable teams to manage the lifecycle of their services while adhering to established deployment standards. * Build tooling to enable engineering teams to own the application deployment process through CI/CD pipelines using GitHub Actions. * Drive security across every layer of the stack, using industry\-standard tools to detect vulnerabilities and anomalies and automating SOC 2 compliance controls. * Extend security controls to AI and agentic systems, covering risks such as prompt injection, over\-permissioned tool access, and sensitive data exposure. * Advance observability with OpenTelemetry and Grafana Cloud across services, infrastructure, and AI workloads. * Build developer tooling for local and remote container\-based development, plus automated production\-like scenarios for testing and release validation. * Build and operate infrastructure for ML and internal AI workloads, including compute for model training, hosting for agents and MCP servers, and least\-privilege access to internal data. * Partner with the Data team on core data infrastructure, including our Databricks data lake and Kafka event streams (Aiven/AWS). * Contribute to building a highly available, web\-based depot operations platform that supports the future of EV charging using NodeJS. * Participate in a 24/7 on\-call rotation and lead incident response and blameless postmortems. ### Minimum Qualifications * 8\+ years building and operating high\-availability production systems, including leading DevOps, SRE, or platform engineering initiatives. * Deep experience with building and maintaining AWS, managed Kubernetes (EKS), and infrastructure as code (Terraform). * Experience building CI/CD pipelines (e.g., GitHub Actions) and observability with tools such as OpenTelemetry and Grafana. * Experience leading SRE or DevOps initiatives supporting production cloud platforms. * Experience with managed databases such as PostgreSQL or MongoDB. * Strong communication skills and the ability to collaborate effectively across engineering, product, and infrastructure teams. ### Preferred Qualifications We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us. Work Location: Canada Availability: Full time

Job Title: Senior Applied ML/AI Scientist - Search Company: Faire Job Category: Machine Learning Roles & Responsibilities: About The Role Search is how retailers do their jobs on Faire. Wholesale queries and retailer expectations look different from consumer e\-commerce and the right product depends on the store's category, price point, and aesthetic. When we get it wrong, it costs real money. Hybrid Faire employees currently go into the office 3 days per week on Tuesdays, Thursdays, and a third flex day of their choosing (Monday, Wednesday, or Friday).* Additionally, hybrid in\-office roles will have the flexibility to work remotely up to 4 weeks per year. Specific Workplace and Information Technology positions may require onsite attendance 5 days per week as will be indicated in the job posting.* Why you’ll love working at Faire * Move fast: You'll own meaningful problems that serve customers around the globe with the agency to move fast and see your results clearly. * Equipped to scale: We invest in what matters, including the latest enterprise AI tools, to help you work smarter and get more out of every day. * Best in class: Our team is full of sharp, kind, and generous colleagues who care about their craft and about helping you grow in yours. * Real rewards. Competitive pay, equity, and comprehensive benefits designed to support your life inside and outside of work. * Belonging: We're intentional about building an environment where every Faire employee has equal access to opportunities, growth, and success. Faire was founded in 2017 by a team of early product and engineering leads from Square. We’re backed by some of the top investors in retail and tech including: Y Combinator, Lightspeed Venture Partners, Forerunner Ventures, Khosla Ventures, Sequoia Capital, Founders Fund, and DST Global. We have headquarters in San Francisco and Kitchener\-Waterloo, and a global employee presence across offices in Toronto, London, and New York. To learn more about Faire and our customers, you can read more on our blog. Work Location: Kitchener, Ontario, Canada Availability: Full time

Job Title: Machine Learning Operations Developer, AI/ML Platform Company: Autodesk Job Category: Machine Learning Roles & Responsibilities: 26WD101494 Machine Learning Operations Developer, Inference, AI/ML Platform Autodesk, a global leader in 3D design, engineering, manufacturing, and entertainment software, is seeking an MLOps Developer to join our AI/ML Platform team. This role will contribute to ensuring the smooth operationalization of machine learning models and the overall efficiency of our next\-generation AI/ML platform used in the development of machine learning and generative AI solutions powering Autodesk’s suite of products and services. You will collaborate with research and product engineering teams from various domains including design, construction, manufacturing, and media \& entertainment to support platform operations. The role is hybrid Toronto or Montreal. Responsibilities * Contribute to the operational excellence of our AI/ML Platform by implementing and optimizing MLOps practices * Develop and maintain automated deployment pipelines for machine learning models, supporting seamless transitions from development to production * Collaborate with cross\-functional teams to design, implement, and maintain scalable infrastructure for model training, inference, and data processing * Help develop and maintain monitoring and logging systems to track model performance, system health, and overall platform efficiency * Work closely with data developers to ensure efficient data pipelines for model training and validation * Implement version control systems for machine learning models and contribute to model governance practices * Contribute to the implementation of robust model governance practices, version control systems, and adherence to compliance standards. Support data privacy and ethical considerations, fostering trust in our AI/ML solutions * Implement security best practices and compliance standards in all aspects of MLOps, ensuring data privacy and platform security * Identify opportunities for process automation and optimization, and implement strategies to enhance the overall MLOps lifecycle * Participate in identifying and resolving operational issues, contributing to incident response and system recovery * BS or MS in Computer Science, or related field * 2\+ years of hands\-on experience in DevOps and MLOps, with a focus on deploying and managing machine learning models in production environments * Experience implementing Infrastructure as Code practices using tools such as Terraform or Ansible * Experience with containerization technologies (Docker, Kubernetes) for orchestrating and scaling machine learning workloads * Experience setting up and maintaining Continuous Integration and Continuous Deployment (CI/CD) pipelines for machine learning projects * Experience scripting in Python, Bash, or similar languages for automating operational processes * Familiarity with monitoring and logging tools (e.g., Prometheus, Grafana, ELK Stack) for tracking system and model performance * Understanding of security best practices in MLOps, including data encryption, access controls, and compliance standards * Excellent collaboration and communication skills, working effectively with cross\-functional teams including data developers, software developers, and researchers * Ability to troubleshoot and resolve operational issues in a timely manner * Experience with cloud platforms, especially AWS or Azure, for deploying and managing machine learning infrastructure * Familiarity with databases and data storage solutions commonly used in MLOps, such as SQL, NoSQL, or data lakes * Exposure to popular machine learning frameworks (TensorFlow, PyTorch) and their integration into MLOps processes * Previous experience with collaboration tools like Git for version control and Jira for project management * Familiarity with Agile development methodologies and working in an iterative, collaborative environment This role may require in\-person onboarding and/or in\-person ID verification. Work Location: Toronto, Canada Availability: Full time

Job Title: Applied Researcher 1 - Multimodal AI Company: eBay Inc. Job Category: Machine Learning Roles & Responsibilities: JOB DESCRIPTION At eBay, we're more than a global ecommerce leader — we’re changing the way the world shops and sells. Our platform empowers millions of buyers and sellers in more than 190 markets around the world. We’re committed to pushing boundaries and leaving our mark as we reinvent the future of ecommerce for enthusiasts. About the role and the team: As an Applied Researcher 1 within the Computer Vision team, you will contribute to advancing eBay’s vision and multimodal capabilities for large\-scale applications. You will work on training, fine\-tuning, evaluation, and deployment of models across problem spaces including search, recommendations, content generation, and video understanding. This role is a hands\-on researcher role where you will contribute across key stages of projects, apply rigorous methodology, and help bring high\-quality ML solutions from idea to production. * Develop and deploy computer vision or multimodal machine learning models for production applications across search, recommendations, content generation, and video. * Contribute across key stages of research and applied ML projects, from problem framing and data preparation through experimentation, evaluation, implementation, and productionization. * Build and improve training, inference, and evaluation pipelines for modern AI systems, including workflows that orchestrate multiple models and services. * Apply strong experimental methodology and scientific rigor to model development, including metric design, offline and online evaluation, error analysis, and ablation studies. * Contribute to core capabilities in classical and modern vision or multimodal problem spaces such as detection, segmentation, classification, contrastive learning, visual\-language modeling, and multimodal retrieval. * Build and improve agentic or VLM\-powered pipelines that combine foundation models, retrieval, tools, and business logic to deliver scalable user and seller experiences. * Partner with product managers, engineers, designers, and researchers to translate business opportunities into practical ML solutions. * Communicate progress, technical trade\-offs, and results clearly to cross\-functional stakeholders. Work Location: Toronto, Canada Availability: Full time

Job Title: Senior Machine Learning Systems Engineer, LLM Inference Performance on AWS Trainium Company: Capgemini Engineering Job Category: Machine Learning Roles & Responsibilities: At Capgemini Engineering, the world leader in engineering services, we bring together a global team of engineers, scientists, and architects to help the world’s most innovative companies unleash their potential. From autonomous cars to life\-saving robots, our digital and software technology experts think outside the box as they provide unique R\&D and engineering services across all industries. Join us for a career full of opportunities. Where you can make a difference. Where no two days are the same. We are seeking a hands\-on machine learning systems engineer to optimize production\-grade large language model inference across current and next\-generation AWS Trainium platforms, including Trainium2 and Trainium3\. The work focuses on decode latency, throughput under representative load, price\-performance, long\-context and multi\-chip scaling, and reproducible benchmarking against leading GPU platforms. #### Your role * Improve decode latency, throughput, and cost on AWS Trainium platforms, validating correctness at every step. Initial priorities include speculative decoding and native multi\-token prediction (MTP) for Qwen\-family models in vLLM on AWS Neuron. * Design and run matched, reproducible tests against leading GPU platforms, controlling prompts, concurrency, cache state, and warm\-up. Deliver latency\-throughput Pareto frontiers and price\-performance comparisons aligned with customer service\-level objectives. * Benchmark and migrate workloads across Trainium generations, including Trainium2 and Trainium3\. Scale serving on multi\-chip Trainium systems for long\-context workloads, including contexts of 100K tokens or more, and apply architecture\-specific optimizations. * Extend model coverage. Onboard additional architectures, including hybrid state\-space and mixture\-of\-experts models, reusing and generalizing existing implementations where practical. * Create runbooks, implementation guides, and benchmark documentation, and support hands\-on workshops so customer engineers can operate, reproduce, and tune the stack independently. #### Your skills and experience Capgemini is an Equal Opportunity Employer encouraging inclusion in the workplace. Capgemini also participates in the Partnership Accreditation in Indigenous Relations (PAIR) program which supports meaningful engagement with Indigenous communities across Canada by promoting fairness, accessibility, inclusion and respect. We value the rich cultural heritage and contributions of Indigenous Peoples and actively work to create a welcoming and respectful environment. All qualified applicants will receive consideration for employment without regard to race, national origin, gender identity/expression, age, religion, disability, sexual orientation, genetics, veteran status, marital status or any other characteristic protected by law. Capgemini is a global business and technology transformation partner, helping organizations to accelerate their dual transition to a digital and sustainable world, while creating tangible impact for enterprises and society. It is a responsible and diverse group of 340,000 team members in more than 50 countries. With its strong over 55\-year heritage, Capgemini is trusted by its clients to unlock the value of technology to address the entire breadth of their business needs. It delivers end\-to\-end services and solutions leveraging strengths from strategy and design to engineering, all fueled by its market leading capabilities in AI, generative AI, cloud and data, combined with its deep industry expertise and partner ecosystem. Work Location: Toronto, Canada Availability: Full time

Job Title: Machine Learning Engineer Company: Point11 Job Category: Machine Learning Roles & Responsibilities: We are a team building the infrastructure for the agent era. A decade ago, the brands that leaned into mobile won. The same shift is happening with agents: how people find businesses, ask questions, and buy is moving from static pages into conversations. 800 million people already discover brands through agent search, and who gets chosen is being decided right now. About You * You've trained and shipped machine learning models that run in production * You're strong in Python and the modern ML stack * You're rigorous about evaluation, baselines, and statistical validity * You can explain a model's behavior to an engineer and to a customer * You're in Toronto or willing to relocate. This role is hybrid, in our Toronto office three days a week. This posting is for an existing vacancy. * Your models decide how faithful our product is * Hybrid in Toronto, in New York's time zone * A founding role in Point11's Canadian team * A direct line to the founding team on product * An annual learning budget for conferences and development Point11 provides equal employment opportunities to current and prospective employees, without regard to race, color, religion, sex, gender identity, national origin, sexual orientation, age, pregnancy, disability, marital status, or any other status protected by the laws of the country where the role is based. Work Location: Greater Toronto Area, Canada Availability: Full time

Job Title: Machine Learning & Algorithms Engineer Company: Lisplogics Job Category: Machine Learning Roles & Responsibilities: Lisplogics is a Montreal\-based company that builds real\-time optimization systems for business operations. We use operations research to help organizations plan and manage their operations as they happen. Our systems run in production and are used daily by our clients. The role What you will do * Translate client operational problems into mathematical models * Design and implement optimization algorithms (linear and mixed\-integer programming, heuristics, metaheuristics) * Build machine learning models for forecasting and decision support * Test, benchmark and improve models on real data * Work with our engineering team to deploy models in production * Present results to clients and explain the reasoning behind them Work Location: Montreal, Quebec, Canada Availability: Full time

Job Title: Senior Machine Learning Engineer Company: Fitch Group, Inc. Job Category: Machine Learning Roles & Responsibilities: Senior Machine Learning Engineer – AI Innovation Teams As one of the world’s top three credit ratings agencies, Fitch Ratings plays a critical role in global capital markets by providing credit analysis, ratings, research, and commentary to financial market participants. For over 100 years, Fitch Ratings has been creating value for global markets through its rigorous analysis and deep expertise, which have resulted in a variety of market leading tools, methodologies, indices, research, and analytical products. Fitch Ratings is part of Fitch Group, a global leader in financial information services with operations in more than 30 countries, which also includes Fitch Solutions. With dual headquarters in London and New York, Fitch Group is owned by Hearst. Join our fast‑growing Toronto innovation hub, where we’re building production‑grade AI to reshape global credit decisions. Here, you won’t just ship features—you’ll help reinvent an industry using decades‑deep proprietary data and a modern tech stack, free from heavy legacy. Backed by full enterprise support, you’ll build reliable, explainable intelligence at real scale within a collaborative, outcome‑driven community. If you’re looking for purposeful work, constant learning, and a place where your impact and growth accelerate—this is where you’ll thrive. Fitch Ratings is seeking a Senior Machine Learning Engineer to join our new AI Innovation teams in Toronto—where we're building the AI\-powered future of financial analysis from the ground up. This isn't about tweaking hyperparameters or maintaining legacy models. This is about designing and shipping breakthrough generative AI systems, agentic workflows, and intelligent platforms that will transform how credit analysis happens and how global financial markets operate. We need ML engineers who thrive on hard problems and greenfield opportunities—whether you're passionate about pushing the boundaries of what LLMs can do, excited to architect intelligent systems that reason and act, or energized by turning cutting\-edge research into production capabilities. If you're motivated by "let's build this and see what's possible" rather than "let's wait and see what others do," this is a high\-impact role where you'll spend your time shipping transformative ML systems—working alongside talented engineers who are equally committed to building something significant. What We Offer * Build breakthrough ML systems with real impact – Design and ship production generative AI platforms, multi\-agent systems, and intelligent automation that will process billions in credit decisions; work on problems at the frontier of applied AI while seeing your work directly change how analysts and financial markets operate * Work at the cutting edge of ML technology – Experiment with the latest LLMs and foundation models, implement novel RAG architectures, build agentic systems, fine\-tune neural networks, and leverage enterprise\-scale GPU clusters and cloud infrastructure; substantial conference and training budgets to stay at the forefront * Technical leadership without the bureaucracy – Lead projects, mentor junior engineers, shape technical direction, and influence architectural decisions through your expertise and results—not through management hierarchy; your ideas and code will define how we build AI systems * Toronto's world\-class AI ecosystem – Work in one of the world's premier AI research hubs alongside Vector Institute researchers, attend cutting\-edge ML meetups and conferences, and be part of the community defining the future of applied AI and machine learning * Greenfield innovation with enterprise backing – Build net\-new ML systems from scratch with the freedom to experiment boldly, fail fast, and push boundaries—backed by the compute resources, research budgets, and organizational support that most startups can only dream of * Solve sophisticated ML challenges – Tackle hard problems at the intersection of NLP, document intelligence, reasoning systems, agentic workflows, and production\-scale deployment; work on challenges that will expand your expertise and push your technical boundaries * Clear growth trajectory – High visibility to senior leadership, mentorship from experienced ML architects, and clear paths to Lead ML Engineer or Principal ML Engineer roles; build a reputation as a go\-to expert in generative AI and financial technology We'll Count on You To: * Design and build transformative ML systems – Lead the development of advanced generative AI solutions, agentic workflows, RAG architectures, and intelligent platforms using PyTorch, modern ML frameworks, and large language models; write production\-quality code that scales and performs * Drive AI innovation through hands\-on technical work – Experiment with frontier models, implement novel ML architectures, evaluate emerging AI technologies, build proofs\-of\-concept, and translate cutting\-edge research into production capabilities that deliver real business value * Lead projects and drive technical excellence – Take ownership of significant ML initiatives from design through deployment; make architectural decisions, establish coding standards, implement robust CI/CD for ML systems, and ensure solutions are both innovative and reliable * Mentor and elevate junior engineers – Provide technical guidance, conduct code reviews, share ML best practices, and help junior team members grow their skills; foster a culture of learning, experimentation, and technical excellence * Build production ML infrastructure – Develop scalable APIs (FastAPI, etc.) for model deployment, implement MLOps pipelines, leverage cloud platforms (AWS/Azure) to optimize AI infrastructure, and use orchestration tools (Airflow) for complex ML workflows * Champion ML governance and operational excellence – Ensure adherence to AI/ML governance guidelines, monitor model performance and SLAs, optimize systems for reliability and cost\-effectiveness, and implement best practices for production ML systems * Collaborate across teams effectively – Partner with product squads, business stakeholders, and cross\-functional teams to integrate ML solutions into flagship products and workflows; translate complex ML concepts for diverse audiences; and ensure seamless transitions from prototype to production * Stay at the bleeding edge – Continuously explore emerging ML technologies, attend conferences, contribute insights from research, and bring innovative approaches back to the team; help shape our technical strategy through your expertise and experimentation What Would Make You Stand Out: * Cutting\-edge AI implementation experience – Track record of taking breakthrough AI capabilities from research/prototype to production\-scale deployment; experience supporting seamless transitions from experimentation to enterprise\-grade ML systems * Multi\-agent and agentic systems expertise – Hands\-on experience building sophisticated multi\-agent systems, agentic workflows, tool\-using AI, or complex AI orchestration platforms that demonstrate advanced reasoning and autonomy * MLOps and cloud\-native infrastructure – Advanced expertise building ML infrastructure, sophisticated MLOps pipelines, model serving platforms, and optimizing cost/performance of production LLM deployments at scale * Document and content management systems experience – Experience developing or integrating ML functionality for document management systems, content platforms, or document intelligence solutions * Technical thought leadership – Contributions to open source ML projects, conference presentations, published research, blog posts about practical ML applications, or active participation in the ML engineering community * Financial services or analytical domain knowledge – Understanding of credit analysis workflows, regulatory requirements, financial data products, or how ML enables better financial decision\-making; familiarity with ratings agencies is valuable * Startup or innovation environment experience – History of building greenfield ML products, working in fast\-paced AI innovation teams, or being part of early\-stage initiatives where you helped shape technical direction and culture * Toronto AI/ML community involvement – Active participation in Toronto's AI/ML engineering or research communities, connections to academic groups, or strong interest in contributing to Toronto's world\-class AI ecosystem About Fitch Group Fitch Group is a global leader in financial information services with operations in more than 30 countries. Wholly owned by the Hearst Corporation, we are comprised of three main businesses: Fitch Ratings \| Fitch Solutions \| Fitch Learning. Fitch is committed to providing global securities markets with objective, timely, independent and forward\-looking credit opinions. To protect Fitch's credibility and reputation, our employees must take every precaution to avoid conflicts of interests or any appearance of a conflict of interest. Should you be successful in the recruitment process at Fitch Ratings you will be asked to declare any securities holdings and other potential conflicts prior to commencing employment. If you, or your immediate family, have any holdings that may conflict with your work responsibilities, you may be asked to divest yourself of them before beginning work. Work Location: Toronto, Ontario, Canada Availability: Full time