Job Title: Principal AI/ML Engineer Company: Mobileum Job Category: Machine Learning Roles & Responsibilities: Mobileum is a leading provider of Telecom analytics solutions for roaming, core network, security, risk management, domestic and international connectivity testing, and customer intelligence. More than 1,000 customers rely on its Active Intelligence platform, which provides advanced analytics solutions, allowing customers to connect deep network and operational intelligence with real\-time actions that increase revenue, improve customer experience, and reduce costs. Reporting to: Manager \- AI/ML Engineering Type: Individual contributor (technical lead) About the Role The AI/ML Development team executes Business Unit product roadmaps, delivering traditional machine learning solutions and implementing agentic AI capabilities where the business and products need them. We are a technology vendor serving the Telecom industry, and we are looking for a Principal AI/ML Engineer to be a senior technical owner of the ML solutions behind our products. The role is a hybrid that combines data science (developing and validating models) and ML engineering (building the training and inference pipelines and services that run them in production). Roles \& Responsibility: Data science and model development * Design and evolve end\-to\-end training and inference pipelines for the core product ML solutions, from data processing through deployment. * Build production inference pipelines in PySpark and inference services with REST endpoints, and tune Spark jobs to cut processing time and infrastructure cost at telecom data volumes. * Apply MLOps practices (monitoring, retraining, versioning, testing, deployment automation) that work across different customer environments. * Use AI and agentic AI to improve development and operational processes (AI\-assisted coding and review, testing, documentation, monitoring, data quality checks). * Enhance traditional ML solutions with agentic capabilities where they add value, using shared platform components from the AI Innovation \& Platforms team. Technical leadership * Set technical standards for code and model review, testing, and reusable components, including guardrails for AI\-assisted workflows. * Mentor junior engineers and data scientists, and technically lead selected projects. * Work with product, engineering, BU stakeholders, and telecom domain experts to keep delivery aligned with roadmap commitments. * Technical Leadership \& Mentoring * Problem Framing \& Ownership * Analytical \& Critical Thinking * Communication of Technical Trade\-offs * Cross\-functional Collaboration * Pragmatic Decision\-Making under Constraints * Learning Agility \& Innovation Work Location: Bengaluru, Karnataka, India Availability: Full time
Job Title: Senior Consultant | Backend Developer | Mumbai | Engineering | Platform Development & Integration Company: Deloitte Job Category: Machine Learning Roles & Responsibilities: Senior Consultant \| Backend Developer \| Mumbai \| Engineering \| Platform Development \& Integration Deloitte’s Technology \& Transformation practice can help you uncover and unlock the value buried deep inside vast amounts of data. Our global network provides strategic guidance and implementation services to help companies manage data from disparate sources and convert it into accurate, actionable information that can support fact\-driven decision\-making and generate an insight\-driven advantage. Our practice addresses the continuum of opportunities in business intelligence \& visualization, data management, performance management and next\-generation analytics and technologies, including big data, cloud, cognitive and machine learning. Roles \& Responsibilities * Lead the design and development of agentic and intelligent systems with strong ownership of core algorithms, model behavior, and decision logic, minimizing reliance on managed AI services and abstraction\-heavy frameworks. * Architect end\-to\-end agentic systems with deep control over model internals, reasoning flows, tool\-use logic, and execution pipelines rather than wrapper\-based orchestration. * Take algorithmic and model\-level ownership of agent behavior, including reasoning strategies, planning algorithms, fallback logic, and failure handling. * Design and implement custom agent orchestration, memory systems, context management, and stateful workflows without over\-dependence on LangChain/LangGraph\-style abstractions. * Build and fine\-tune open\-source and self\-hosted LLMs or hybrid approaches (open\-weight \+ proprietary) based on performance, cost, and security trade\-offs. * Develop custom RAG pipelines, embedding strategies, ranking, re\-ranking, and grounding mechanisms tailored to enterprise knowledge systems. * Integrate agentic solutions with enterprise applications, APIs, data platforms, and internal ML systems. * Establish low\-level design patterns, reusable libraries, and internal frameworks for building agent\-based systems from first principles. * Optimize model inference, reasoning efficiency, latency, cost, and response quality, including prompt\-free and programmatic reasoning approaches where applicable. * Drive Responsible AI practices with in\-house guardrails, explainability techniques, auditability, and governance controls. * Collaborate with business stakeholders to translate complex requirements into deterministic, reliable agent\-driven solutions, avoiding black\-box dependencies. * Lead production support, deep debugging, RCA, and performance tuning at both system and model levels. * Mentor junior consultants on core AI concepts, algorithms, and system design, not just framework usage. * 4 to 7 years of strong experience building AI\-driven systems, with demonstrated ownership of model behaviour and algorithms, not just API integration. * Deep understanding of agentic architectures, multi\-agent coordination, autonomous decision\-making systems, and custom orchestration approaches. * Ability to design agent frameworks from scratch or significantly extend existing ones rather than relying out\-of\-the\-box abstractions. * Strong understanding of LLM internals, transformer architectures, inference behavior, tokenization, attention, trade\-offs. * Experience with open\-source LLMs (e.g., LLaMA\-family, Mistral, Mixtral, Falcon, Qwen) and self\-hosted deployments. * Advanced reasoning strategies: chain\-of\-thought alternatives, tree/graph\-based reasoning, planners, critic–agent loops, tool reasoning. * Prompting as a controlled technique, not the primary abstraction. * Experience with LangChain, LangGraph, AutoGen, CrewAI as reference tools, with proven ability to bypass, customize, or replace them with custom implementations. * Preference for code\-first, algorithm\-driven approaches over declarative or black\-box pipelines. * Deep experience in RAG pipelines, embeddings, vector similarity search, hybrid retrieval, reranking models, and knowledge grounding. * Strong grasp of information retrieval, indexing, and retrieval algorithms. * Strong systems engineering skills with Python and/or Java/Go for AI backends. * Experience deploying AI workloads on GCP, including self\-managed inference, GPUs, containers, and scaling strategies. * Familiarity with MLOps practices: model versioning, evaluation pipelines, controlled rollouts, and experimentation. * Deep experience in AI observability: tracing reasoning paths, latency profiling, quality evaluation, and cost monitoring. * Strong understanding of AI security, data privacy, access control, and compliance, with preference for in\-house controls over managed tooling. * General Insurance (Health, Motor, Travel) domain knowledge is a strong advantage. Work Location: Maharashtra, India Availability: Full time
Job Title: Data Scientist - Computer Vision Company: Philips Job Category: Machine Learning Roles & Responsibilities: Key Responsibilities AI/ML solution development * Analyse complex and high\-dimensional datasets and formulate hypotheses and proof\-of\-concepts. * Design, develop and evaluate Machine Learning and Deep Learning models . * Build solutions using Computer Vision, NLP, Generative AI and LLM\-based techniques , depending on the use case. * Evaluate state\-of\-the\-art AI/ML approaches and apply appropriate techniques to complex business problems. · Develop AI\-based Computer Vision applications for image\-related use cases. * Apply modern deep\-learning architectures and frameworks, including approaches such as YOLO and U\-Net , where appropriate. * Work with image\-processing and feature\-extraction techniques and develop robust model\-training and evaluation pipelines. · Develop solutions using LLMs, Transformers, RAG architectures and agentic frameworks . * Work closely with engineering teams to integrate AI models into production applications and enterprise workflows. * Monitor and continuously improve deployed model performance. * Contribute to robust and scalable data pipelines supporting advanced analytics and ML solutions. this role is an office role. Work Location: Karnataka, India Availability: Full time
Job Title: Senior, Platform Engineer Company: GSK Job Category: Machine Learning Roles & Responsibilities: About the Role: GSK's Enterprise AI team builds and deploys AI solutions at scale. Behind every AI product shipped is a shared platform: the Accelerator platform estate, GitHub Copilot, Claude Code/Cowork, Databricks AI Assistant, and Google Cloud data and AI services (BigQuery, Vertex AI, and GKE or Cloud Run). The Senior Platform Engineer (G6\) runs this estate, applying Site Reliability Engineering principles, managing strategic vendor relationships, and building the automation and infrastructure\-as\-code foundations that make the delivery roles faster and safer. This senior people\-manager role leads the platform engineering team and sets its technical direction. Most platform work is AI\-augmented: AI coding assistants handle the bulk of routine engineering, freeing the manager and team to focus on architecture, Google Cloud platform strategy, vendor strategy, and reliability. The role's direct customers are the Enterprise AI delivery engineers and the broader population of engineers and data scientists across GSK who build on the platform. Why This Role Exists: Platform availability and developer experience determine how fast Enterprise AI can build and deliver AI solutions. Platform outages or poor developer experience compound across every delivery team in the portfolio. The Senior Platform Engineer (G6\) is accountable for making sure this does not happen, for building the platform leverage that multiplies the productivity of every engineer, and for leading and growing the team that operates the platform. * Runs the platform estate that the delivery roles depend on, with SRE discipline, Google Cloud and vendor management depth, and AI\-native engineering practice. * Builds platform leverage through automation, reusable components, and self\-service capability that reduce repeated engineering work across the portfolio. * Leads and grows the platform team by hiring, coaching, and developing engineers toward greater platform ownership, and by raising the team's capability over time. Key Responsibilities: * Run the Google Cloud and SaaS platforms that Enterprise AI uses to build AI solutions, including BigQuery, Vertex AI, GKE or Cloud Run, and the Accelerator platform. Ensure availability, security, and cost efficiency. * Own Google Cloud platform architecture: design and operate BigQuery and Vertex AI environments, including partitioning, reservations, and cost controls, plus Pub/Sub and Dataflow ingestion and Cloud Composer orchestration. * Apply SRE principles: service\-level objectives, observability (Cloud Monitoring, OpenTelemetry), capacity planning, and vendor incident escalation across the platform estate. * Use AI coding assistants as the default mode of work; build automations that handle routine platform tasks at scale, using Claude Code, Codex, GitHub Copilot, and Databricks AI Assistant. * Manage the platform estate through Infrastructure\-as\-Code (Terraform, GitOps, and CI/CD) so configurations are versioned, auditable, and reproducible across Google Cloud and other platforms. * Treat platform users as customers by delivering self\-service capabilities, clear documentation, and fast onboarding for FDEs, FSEs, and data scientists. * Embed security, data privacy, and AI governance controls so AI solutions inherit compliance rather than building it from scratch. Set the Google Cloud security baseline: IAM and least\-privilege access, Cloud KMS (CMEK), VPC Service Controls, Secret Manager, and Organization Policies. Vendor Management \& Standards: * Act as the technical counterpart to strategic SaaS and cloud vendors (Microsoft, Anthropic, Databricks, and Google) on roadmap, capacity, integration, and incident escalation. Lead the Google Cloud relationship for data and AI services. * Continuously evaluate emerging AI tooling: pilot what looks promising through fast sandbox proofs of concept, and make adoption or deferral decisions. * Build and maintain reusable platform components so delivery roles do not reinvent the wheel across engagements. Team Leadership \& Reporting: * Lead and develop the platform engineering team by hiring, setting objectives, running 1:1s and performance reviews, and developing junior engineers toward greater platform ownership. * Track and report AI tooling adoption against the organisation's adoption goals for Enterprise AI leadership, and manage platform budget and cloud cost (FinOps) across Google Cloud and SaaS spend. * Own team delivery and prioritisation: set the team's roadmap and prioritise platform work against delivery\-team needs, balancing reliability, security, and developer experience. * Overall 0-2 years of experience. * 0-2 years of experience running SaaS or cloud platforms in a production setting. * Strong proficiency in Python and SQL. * Hands\-on experience with Infrastructure\-as\-Code (Terraform), GitOps, and modern CI/CD. * Hands\-on production experience with Google Cloud data and AI services (BigQuery, Vertex AI, GKE or Cloud Run, Pub/Sub, and IAM). * Daily use of AI coding agents (Claude Code, Codex, GitHub Copilot, Databricks AI Assistant) as a primary development mode. * Experience operating production services with SLOs and on\-call practices. * Experience managing or leading engineers, including hiring, performance management, and development. GSK does not accept referrals from employment businesses and/or employment agencies in respect of the vacancies posted on this site. All employment businesses/agencies are required to contact GSK's commercial and general procurement/human resources department to obtain prior written authorization before referring any candidates to GSK. The obtaining of prior written authorization is a condition precedent to any agreement (verbal or written) between the employment business/ agency and GSK. In the absence of such written authorization being obtained any actions undertaken by the employment business/agency shall be deemed to have been performed without the consent or contractual agreement of GSK. GSK shall therefore not be liable for any fees arising from such actions or any fees arising from any referrals by employment businesses/agencies in respect of the vacancies posted on this site. Work Location: Karnataka, India Availability: Full time
Job Title: Sr AI/ML Engineer, Applied AI Company: Thermo Fisher Scientific Job Category: Machine Learning Roles & Responsibilities: Work Schedule Standard (Mon\-Fri) About The Role At Thermo Fisher Scientific, you’ll do meaningful work that makes a positive global impact. Join our colleagues in bringing our Mission to life every single day to enable our customers to make the world healthier, cleaner, and safer. With industry\-leading R\&D investment, we empower our teams to solve complex scientific challenges—from environmental protection to advancing healthcare and cancer research. As a Senior AI/ML Engineer, Applied AI, you will provide hands\-on technical leadership across the design, development, evaluation, and production deployment of advanced AI/ML solutions. You will architect and build machine learning and deep learning models, Large Language Model (LLM) applications, Retrieval\-Augmented Generation (RAG) solutions, and agentic workflows that power internal and external customer\-facing applications. You will work across the AI/ML lifecycle – from ideation, research, and experimentation through data engineering, model development and optimization, evaluation, performance tuning and deployment. Partnering closely with data scientists, software engineers, product teams and scientific stakeholders, you will translate complex business and scientific needs into scalable, reliable, and impactful AI/ML capabilities. This is a deeply hands\-on individual contributor role with significant technical leadership responsibilities. You’ll also mentor engineers, influence platform strategy, and ensure AI\-driven systems are accurate through consistent evaluation frameworks, engineering standards and technical best practices. A successful candidate in this role is expected to collaborate effectively with the broader teams, and consistently deliver well\-architected, production\-grade AI and Generative AI features supporting a variety of use cases and scientific products, with measurable impact on scientific workflows, customer outcomes and innovation velocity. : Key Responsibilities * Lead activities across the AI/ML lifecycle – from ideation, research, data engineering, model development and optimization, evaluation, performance tuning and deployment, while continuously engaging customers to gather feedback and incorporate it into solution development. * Iteratively develop, deploy and scale AI/ML models and solutions across life sciences, genomics, material sciences, and healthcare. * Contribute to the development of AI/ML models and solutions, following established model and system architectures, software design standards, reusable patterns, and best practices for AI and Generative AI solutions. * Apply evaluation\-driven approaches to AI/ML development by implementing and running evaluation frameworks, analyzing model performance, and using results to improve the quality and reliability of AI/ML solutions. * Build and deploy LLM\-powered services using Azure OpenAI, Anthropic Claude, and OpenAI\-compatible APIs. * Architect and implement agentic AI and RAG workflows, including data ingestion, chunking, embeddings, vector search, retrieval, tool calling, memory, and prompt engineering. * Design, develop, and integrate Generative AI systems using LangChain and LangGraph for agentic workflows and orchestration. * Integrate AI/Generative AI capabilities into enterprise platforms, scientific applications and end\-to\-end workflows. * Mentor and guide engineers across the AI/ML lifecycle, including model development, evaluation, and implementation of AI solutions. * Actively participate in Communities of Practice, influencing engineering standards and AI/Generative AI adoption strategies across the organization. * Communicate effectively with technical and non\-technical stakeholders through clear documentation, architecture diagrams and design reviews. * Stay current with advancements in AI/ML, Generative AI, agentic frameworks, and LLM ecosystems, and apply relevant innovations to enhance internal tools, scientific solutions and customer\-facing products. Work Location: Bengaluru East, Karnataka, India Availability: Full time
Job Title: Staff Engineer/AI-ML Engineer Company: The Hartford India Job Category: Machine Learning Roles & Responsibilities: Responsibilities * Research, experiment with, and implement suitable Generative and ML algorithms, tools and technologies. * Participate in identifying and assessing opportunities i.e. value of new data sources and analytical techniques and technology, to ensure ongoing competitive advantage. * Review work with leadership and partners on an ongoing basis to calibrate deliverables against expectations. * Accountable for design, development and maintenance of Models as Service * Work with junior engineers and peers to provide mentorship and thought leadership. Be comfortable presenting new concepts to technical audiences. * Collaborate with partners Enterprise Data, Data Science, Business, Cloud Enablement Team, and Enterprise Architecture teams * Delivery of critical milestones for model deployment in the AWS and GCP clouds. * Adopt and promote MLOps best practices to the Data Science community. Minimum Requirements * Development experience using both the AWS and GCP suite of tools. * Familiarity with SageMaker, Streamlit, web security, credentials and API management tools * Experience developing repeatable architectural patterns; ability to identify redundancies and eliminate them with these patterns. * Experience building and deploying webservices in a cloud environment. * Experience building CICD pipeline using Jenkins or equivalent * Experience with IAC (Infrastructure as Code) including Cloud Formation, Terraform, or similar * Expert\-level Github experience, including Github Actions * Strong object oriented development experience using Python, Java, C\# * Familiarity with big data technologies (i.e. Hadoop, Spark, Hive, etc.) and RDBMS platforms such as Redshift, Snowflake or BigQuery * Experience in end to end model development lifecycle, from ideation through post production monitoring. * Experience with workflow automation platforms (Apache Airflow, Autosys, similar) * Experience with Solution Design and Architecture of data pipelines * Basic understanding of Data Science model development life cycle Work Location: Greater Hyderabad Area Availability: Full time
Job Title: GenAI Architect Company: Persistent Systems Job Category: Machine Learning Roles & Responsibilities: Job Description About Persistent Our disruptor's mindset, commitment to client success, and agility to thrive in the dynamic environment have enabled us to sustain our growth momentum. Persistent has been recognized across top industry platforms for innovation, leadership, and inclusion. We have delivered 26 sequential quarters of growth with $487\.6M in Q4 FY26 revenue, up 7\.8% Q\-o\-Q and 20\.0% Y\-o\-Y. Our 29,500\+ global team members, located in 21 countries, have been instrumental in helping the market leaders transform their industries. We received five awards at the ISG (Information Services Group) Provider Lens Awards 2026 and, for the second year in a row, were awarded the Growth Honor of the Year at the Everest Group Elevate 2026\. We are seeking a highly skilled GenAI Architect to lead the design and implementation of next\-generation AI/ML solutions with a strong focus on GenAI Agents, Agentic AI systems, and Large Language Models (LLMs). The ideal candidate will combine deep technical expertise with architectural leadership to build scalable, production\-grade AI platforms that deliver tangible business outcomes. * Lead the architecture, design, and implementation of enterprise AI/ML and Generative AI solutions. * Design and develop Agentic AI systems and GenAI\-powered applications. * Define AI technology roadmaps aligned with business priorities and innovation goals. * Build scalable architectures integrating Large Language Models (LLMs) with enterprise systems and domain\-specific knowledge sources. * Design Retrieval\-Augmented Generation (RAG) frameworks and knowledge retrieval systems. * Guide development teams in AI engineering best practices, solution design, and deployment strategies. * Evaluate emerging GenAI, Agent Frameworks, LLM orchestration platforms, and AI tooling. * Design AI application workflows, orchestration pipelines, and autonomous agent ecosystems. * Collaborate with business stakeholders to identify AI use cases and business value opportunities. * Establish AI governance, security, scalability, and operational best practices. * Support proof\-of\-concept development and drive production adoption of AI solutions. * Mentor engineers, AI developers, and technical teams. * Stay current with advancements in Generative AI, Agentic AI, Machine Learning, and cloud AI services. Work Location: Maharashtra, India Availability: Full time
Job Title: Junior Data Scientists Company: Siemens Healthineers Job Category: Machine Learning Roles & Responsibilities: Role Job Requirements To contribute to the end\-to\-end development and product lifecycle management of artificial intelligence and machine learning (AI/ML) systems as part of an R\&D organization. The Junior Data Scientist supports the definition of intended algorithm performance, the development and curation of representative data and annotations, the development of AI/ML algorithms, and the generation of scientific evidence demonstrating stand\-alone algorithm performance. The Candidate Is Expected To * Apply scientific, statistical, machine learning, and software engineering methods to develop and evaluate AI/ML algorithm components for defined product use cases. * Work with experienced Data Scientists, product management, clinical or domain experts, software developers, and quality and regulatory functions to translate customer needs and the intended use into measurable algorithm performance targets. * Support the definition, sourcing, curation, documentation, and maintenance of representative data cohorts and annotations required for training, tuning, and evaluation. * Plan and execute reproducible scientific evaluations that provide objective evidence of algorithm performance, limitations, and suitability for the intended use. * Contribute the Data Science work products and evidence required for integration of AI/ML algorithm components into products and for their verification, validation, regulatory release, and lifecycle monitoring. What are my responsibilities? As a Junior Data Scientist, You Are Required To * Support the finalization of product and algorithm concepts by translating customer needs, intended use, clinical or technical requirements, and applicable regulatory considerations into measurable state\-of\-the\-art performance targets. * Contribute to the definition of the AI/ML development and scientific evaluation strategy for assigned algorithm components under the guidance of experienced Data Scientists. * Perform exploratory data analysis and data risk assessments to identify data gaps, quality issues, potential bias, confounding factors, outliers, and limitations that may affect algorithm development or evaluation. * Define and document data needs for training, tuning, and evaluation, including relevant subgroups and acceptance criteria, so that data cohorts are representative of the intended use and aligned with the product and regulatory strategy. * Support the specification of annotations, acceptance criteria, and quality controls; collaborate with qualified internal or external suppliers on sourcing data and annotations. * Curate, version, maintain, and document representative data cohorts and ensure data provenance, traceability, quality, and fitness for purpose together with data management stakeholders. * Design, implement, train, tune, and technically characterize AI/ML algorithm components using appropriate machine learning, deep learning, statistical, or generative AI methods. * Define scientifically justified metrics, evaluation methods, test cases, and acceptance criteria to assess stand\-alone algorithm performance, including relevant aspects such as accuracy, robustness, reliability, generalizability, explainability, bias, and subgroup performance. * Implement and execute reproducible bench tests on representative evaluation cohorts and relevant subgroups as part of the verification and validation strategy. * Analyze and interpret evaluation results, investigate failure modes, document residual limitations, and derive evidence\-based recommendations for algorithm improvement and product decisions. * Document development and evaluation activities and contribute to Data Science work products such as data management information, annotation specifications, scientific evaluation test cases, and scientific evaluation reports. * Provide scientific evidence and technical input for product reviews and regulatory submissions and support responses to questions concerning data, annotations, evaluation methods, and stand\-alone algorithm performance. * Support the design and execution of clinical investigations, reader studies, or other studies when additional evidence is required by the product or regulatory strategy. * Collaborate with software engineering and MLOps functions to establish reproducible pipelines, versioned artifacts, automated tests, controlled deployment, and integration of algorithm components into the product. * Help define and implement monitoring metrics for detecting data anomalies, outliers, and algorithm performance drift across the product lifecycle. * Regularly review advances in Data Science and AI and assess their applicability to the development and evaluation of AI/ML products. Desired Knowledge \& Experience * Sound understanding of machine learning, deep learning, statistical analysis, experimental design, and model evaluation. * Active programming skills in Python and practical experience with libraries and frameworks such as NumPy, Pandas, SciPy, scikit\-learn, PyTorch, TensorFlow, or Keras. * Hands\-on experience with the development workflow of AI/ML models, including data preparation, training, tuning, evaluation, error analysis, reproducibility, and documentation. * Ability to define meaningful performance metrics, evaluation cohorts, subgroup analyses, test methods, and acceptance criteria for the intended use of an AI/ML algorithm. * Understanding of data quality, representativeness, bias, annotation quality, data provenance, and traceability in AI/ML development. * Knowledge of Machine Learning Operations (MLOps) concepts and tooling, including source and data versioning, reproducible pipelines, experiment tracking, automated testing, CI/CD, deployment, and monitoring. * Hands\-on exposure to current Large Language Models and Generative AI, including foundation models, prompting, retrieval\-augmented generation, and model adaptation or fine\-tuning. * Knowledge of Agentic AI, including tool\-using models, multi\-step workflows, orchestration, memory, and autonomous or semi\-autonomous decision paths, is an advantage. * Experience or strong interest in the systematic evaluation of LLM, Generative AI, and Agentic AI systems, including task\-specific benchmarks, groundedness and factuality, robustness, reproducibility, human evaluation, safety and guardrail testing, tool\-use correctness, and analysis of failure modes. * Experience with databases and query languages such as SQL; knowledge of Kusto Query Language, Snowflake, or graph databases such as Neo4j is an advantage. * Exposure to cloud\-based data and AI environments, preferably Microsoft Azure, such as Azure Blob Storage, Azure Databricks, Azure Data Factory, Azure Machine Learning, Azure Monitor, or Azure DevOps. * Experience with structured and unstructured data, for example machine logs, event data, Parquet files, text data, or multimodal data. * Knowledge of healthcare, medical technology, clinical data, or interoperability standards such as DICOM is preferred. * Basic project management skills and familiarity with working in cross\-functional R\&D or product\-development teams. Required Soft Skills \& Other Capabilities * Scientific mindset, strong analytical ability, and attention to detail. * Commitment to objective evidence, reproducibility, traceability, data integrity, and responsible AI development. * Curiosity and resilience to investigate unexpected results, challenge assumptions, and improve models based on evidence. * Collaborative approach to working with Data Scientists, domain experts, software engineers, product management, and quality and regulatory stakeholders. * Ability to work independently within an agreed scope and to seek guidance when technical, scientific, product, or regulatory implications require alignment. * Clear written and verbal communication skills in English, including the ability to explain methods, results, uncertainties, limitations, and risks to technical and non\-technical stakeholders. Work Location: Bengaluru, Karnataka, India Availability: Full time
Job Title: Machine Learning Engineer - Agentic AI & Reinforcement Learning Company: Viridien Job Category: Machine Learning Roles & Responsibilities: Who You'll Work With As a member of our AI Lab, you'll collaborate with senior machine learning engineers, software developers, HPC specialists and scientific domain experts across Viridien. Learn more about the team on our website, in a recent blog post featuring one of our senior machine learning engineers and from our Voices of Viridien. What You'll Work On * Designing and developing single\-agent and multi\-agent systems. * Implementing and evaluating capabilities such as reasoning, planning, task decomposition, memory, tool use and agent collaboration. * Integrating LLMs, vision\-language models, retrieval systems and domain\-specific tools. * Experimenting with fine\-tuning and adapting foundation models using supervised learning, preference optimisation and reinforcement learning where appropriate. * Developing training environments, reward functions, graders and feedback mechanisms. * Designing evaluations, guardrails and monitoring approaches for agent reliability. * Helping turn successful prototypes into APIs, services and reusable software components. Learning and development At Viridien, we foster a culture of continuous learning and provide tailored training programmes through our Learning Hub, designed to enhance technical, commercial, and personal growth. We care for the environment We encourage and actively support a strong sense of community, through volunteering and various company initiatives. As a company we are also committed to reducing our environmental impact, improving energy efficiency and accelerating sustainable solutions. Work Location: Thane, Maharashtra, India Availability: Full time
Job Title: Associate Data Scientist Company: TriNet Job Category: Machine Learning Roles & Responsibilities: Don't meet every single requirement? Studies have shown that many potential applicants discourage themselves from applying to jobs unless they meet every single requirement. TriNet always strives to hire the most qualified candidate for a particular role, ensuring we deliver outstanding results for our small and medium\-size customers. So if you're excited about this role but your past experience doesn't align perfectly with every single qualification in the job description, nobody’s perfect – and we encourage you to apply. You may just be the right candidate for this or other roles. A Brief Overview The Associate Data Scientist is a core contributor within the Enterprise Data \& Analytics team, responsible for solving well\-defined analytical and machine learning problems through strong technical execution. You will work closely with senior data scientists and data engineers to support the development, validation, and implementation of models across use cases. The primary responsibility of the Associate Data Scientist is to execute well\-defined analytical tasks and model development activities based on clear guidance and established workflows. The role emphasises accuracy, consistency, and disciplined validation of outputs, ensuring that models and analyses meet defined technical requirements. Over time, you will act as a foundation for developing more advanced analytical and problem\-solving capabilities. What You Will Do Analytical Modelling \& Technical Delivery * Develop, test, and implement statistical and machine learning models under guidance from senior team members * Apply standard techniques such as regression, classification, clustering, and forecasting to pre\-defined problem statements * Ensure models are reliable, interpretable, and aligned with defined objectives Execution of Defined Analytical Tasks * Work on clearly defined analytical problem statements provided by senior team members * Support hypothesis testing, feature engineering, and structured analysis under guidance * Perform data cleaning, transformation, and preparation for modelling * Conduct exploratory data analysis to identify patterns, trends, and anomalies * Assess data quality issues and support data readiness for modelling * Validate model performance using standard metrics and evaluation techniques * Ensure outputs are accurate, reproducible, and aligned with defined expectations * Support testing and debugging of analytical models * Gain exposure to structured and unstructured data, including NLP and LLM\-based techniques * Support experimentation activities such as prompt execution, output validation, and model testing * Work closely with data scientists and senior team members within the team * Follow coding standards, documentation practices, and defined development processes * Contribute to documentation of models, experiments, and outputs Work Location: Hyderabad, Telangana, India Availability: Full time
