Job Title: Senior Data Scientist (Credit) Company: Moniepoint Group Job Category: Machine Learning Roles & Responsibilities: Who we are Moniepoint is a global fintech building modern financial services for millions of people and businesses across high\-growth markets. We provide payments, banking, credit, and financial management tools \- reliable products that people and businesses use every day to run their lives, grow their companies, and move money safely. About the role: At Moniepoint, data is at the core of everything we do. We are a customer\-centric company, and your work will enable our teams to make informed, data\-driven decisions that directly impact the success of our business. We are seeking a data\-driven, impact\-oriented Senior Data Scientist to power Moniepoint’s consumer lending decisions at scale. You will build the models, experiments, and insights that shape how we acquire, underwrite, and retain millions of customers across Nigeria and Africa. This role is pivotal in driving our pricing, credit limit modelling, and credit model deployment strategy in a growing business with unparalleled opportunities to give access to millions of customers. You’ll collaborate with our amazing Consumer Credit team and drive how we think about risk ranking, product offering, and data infrastructure to create the best personalised offers for our customers. You’ll be responsible for building our first suite of credit risk models and help drive a culture of how we should think about machine learning vs more traditional methods \- bringing fresh thinking to inspire both the team and our customers. Curious about what makes Moniepoint an incredible place to work? Check out posts on how we cultivate a culture of innovation, teamwork, and growth.* What You’ll Do * Develop and deploy credit scoring, affordability, and behavioural models to support underwriting, pricing, and collections * Design and run experiments to optimise approval rates, loss rates, and profitability * Partner with product squads to embed decision logic into real\-time systems * Ensure data quality, compliance, and ethical use of models across all decisioning processes * Mentor product squads on best practices in experimentation and data\-driven decision making * Provide models to optimise outcomes in collections, churn management and user retention What to expect in the hiring process * A preliminary phone call with the recruiter * A coding exercise on HackerRank \- covering core data science theory (math, statistics, linear algebra) and Python fundamentals (data structures \& algorithms). * A take\-home assignment * A technical interview with a Lead in our Data Science Team to review your take\-home assignment in depth * A behavioural and technical interview with the hiring manager Work Location: Johannesburg, Gauteng, South Africa Availability: Full time

Job Title: Artificial Intelligence Engineer Company: Tenth Revolution Group Job Category: Machine Learning Roles & Responsibilities: The Role Key Responsibilities * Design and build applied AI and machine learning solutions for client projects. * Develop LLM\-based applications, RAG solutions, AI agents, and workflow automation components. * Build and maintain data pipelines, feature pipelines, model training workflows, and inference services. * Support the productionisation of AI and ML systems using MLOps and LLMOps practices. * Implement monitoring, evaluation, testing, and observability for AI systems. * Integrate AI solutions with APIs, cloud services, data platforms, and enterprise applications. * Work with data scientists, data engineers, software engineers, architects, and delivery teams. * Support technical discovery, solution design, and client discussions when required. * Apply responsible AI principles, including security, privacy, governance, human oversight, and cost control. * Contribute to reusable AI components, frameworks, and delivery accelerators * Python, Pandas, NumPy, Scikit\-learn * XGBoost, LightGBM, CatBoost, Statsmodels, forecasting or optimisation libraries * OpenAI, Azure OpenAI, Anthropic, AWS Bedrock, Google Vertex AI, or similar * LangChain, LangGraph, Semantic Kernel, LlamaIndex, AutoGen, CrewAI, or similar * Vector databases such as Pinecone, Weaviate, Milvus, pgvector, Azure AI Search, or similar * MLflow, Databricks, Unity Catalog, Databricks Model Serving, Feature Store, or Feast * Snowflake, Delta Lake, Apache Spark, Kafka, Confluent Cloud, Airflow, or Databricks Workflows * Azure, AWS, or GCP * Docker, Kubernetes, REST APIs, CI/CD, Terraform * Monitoring and observability tools such as Prometheus, Grafana, ELK, or OpenTelemetry Work Location: South Africa Availability: Full time

Job Title: AI Solutions Engineer Company: IT Consulting Job Category: Machine Learning Roles & Responsibilities: We are seeking a highly skilled and innovative AI Solutions Engineer to join our growing IT Consulting team. The successful candidate will be responsible for designing, developing, implementing, and supporting Artificial Intelligence (AI) and Machine Learning (ML) solutions that address complex business challenges across various industries. This role combines technical expertise, solution architecture, consulting, and client engagement to deliver scalable AI\-powered solutions that drive business value. Key ResponsibilitiesAI Solution Design \& Development * Design, develop, and deploy AI and Machine Learning solutions tailored to client requirements. * Build Proof of Concepts (PoCs), prototypes, and Minimum Viable Products (MVPs). * Develop Generative AI solutions using Large Language Models (LLMs). * Implement Retrieval\-Augmented Generation (RAG) frameworks and AI agents. * Integrate AI capabilities into existing business applications and workflows. * Evaluate and recommend appropriate AI tools, platforms, and technologies. * Architect end\-to\-end AI solutions across cloud and hybrid environments. * Develop scalable, secure, and maintainable AI systems. * Ensure compliance with security, data privacy, and governance requirements. * Design APIs and integration frameworks for AI services. Implementation \& Deployment * Deploy solutions using cloud\-native AI services. * Build CI/CD pipelines for AI and ML workloads. * Monitor and optimize AI model performance and reliability. * Support production deployments and post\-implementation activities. * Troubleshoot and resolve technical issues. * Stay current with emerging AI technologies and industry trends. * Evaluate new AI frameworks, models, and platforms. * Contribute to internal AI accelerators, reusable assets, and best practices. * Support business development and proposal activities. * 4\+ years of software development, solution engineering, or AI implementation experience. * 2\+ years of hands\-on experience developing AI/ML solutions. * Experience in consulting or client\-facing environments preferred. * Experience delivering cloud\-based solutions in enterprise environments. * Machine Learning model development and deployment. * Generative AI and Large Language Models (GPT, Claude, Llama, Gemini). * Prompt Engineering and AI Agent development. * Vector databases and Retrieval\-Augmented Generation (RAG). * Natural Language Processing (NLP). * Computer Vision solutions (advantageous). * Strong analytical and problem\-solving abilities. * Excellent communication and presentation skills. * Ability to engage with both technical and non\-technical stakeholders. * Strong consulting and customer\-facing skills. * Ability to manage multiple projects simultaneously. * Innovative mindset with a passion for emerging technologies. Key Performance Indicators (KPIs) * Work on cutting\-edge AI and digital transformation initiatives. * Collaborate with industry\-leading consultants and technology experts. * Exposure to diverse industries and enterprise clients. * Continuous learning, certification support, and career growth opportunities. * Opportunity to shape and grow the organization's AI practice. Work Location: In person Work Location: Midrand, GP, South Africa Availability: Full time

Job Title: Advanced Analytics: Machine Learning Engineer MMH260511-1 Company: Momentum Job Category: Machine Learning Roles & Responsibilities: Role Purpose Drive the productionisation, operationalisation, and lifecycle management of advanced analytics and machine learning models within Momentum Corporate, with the appropriate incorporation of AI\-based models. The role transforms actuarial and data science prototypes into robust, scalable, auditable production systems in a regulated insurance context \- partnering closely with Actuarial, Data Science, and IT to ensure reliability, explainability, and value at scale. Duties and Responsibilities INTERNAL PROCESS * Actively participates in the design, build and deployment of the solution to a set problem statement by applying advanced analytics, with a particular focus on: * Driving the transformation of actuarial and data science prototypes into production\-grade services (batch jobs and/or APIs), including packaging, testing, documentation, and release. * For mature projects and data pipelines establish Continuous Integration or Continuous Delivery (CI/CD) for ML including scalable, automated scoring pipelines and selecting fit\-for\-purpose architectures (scheduled batch vs. real\-time API). * For new/emerging projects and data pipelines establish tactical data pipelines where appropriate. * Design, deploy, and operate ML pipelines that can leverage Microsoft Fabric artefacts developed by actuarial and other business facing specialists (Lakehouse, Notebooks, Data Pipelines, Warehouse, OneLake). * Partner with Data Engineering and IT to ensure reliable, well\-documented data inputs for training, scoring, and monitoring. * Collaborate with specialists in IT to align on consistent standards and hybrid solutions. * Support the use of machine learning techniques or tools to select features, create and optimise models. Where relevant, aid in the preprocessing of structured and unstructured data, including AI\-based techniques. * Prepare pipelines and deployment patterns that can integrate or leverage off of AI/LLM components (retrieval, orchestration, evaluation) where relevant. * Standardise repository structures, coding conventions, and templates for reusable model components and jobs. * Strategic implementation of ML and AI\-based solutions to enhance model efficiency. * Promote reusability, maintainability, reliability, and scalability in design and development of model solutions. * Embed governance by design: model cards, validation evidence, performance baselines, approvals and sign\-offs for regulated use. * Ensure explainability and auditability of production solutions (feature attributions/diagnostics appropriate to the model class and use case). * Act as the technical interface to IT for environments, network/security patterns, deployment pathways, and operational readiness. * Collaborate closely with actuaries and data scientists to production\-align model design (latency, throughput, cost, explainability, stability). * Provide technical enablement to the analytics team (templates, how\-to guides, code reviews) to uplift production\-ready practices. * Design for cost efficiency across compute, storage, and orchestration; monitor runtime costs and optimise resource utilisation. * Support model performance by catering for monitoring data drift and re\-training systems. * Recommend and implement MLOps tooling (experiment tracking, model registry, orchestration, monitoring) and evolve standards across the team. * Keep current with platform capabilities (e.g. Azure ML vs AWS vs Databricks, containerisation, orchestration services) and introduce pragmatic improvements that reduce risk and lead time. * Collaborating with cross\-functional teams: build and maintain relationships with clients and stakeholders that promote cross\-delivery practice solutions. CLIENT * Maintain strong relationships with Actuarial, Data Science, IT, and business owners to deliver reliable production ML services that meet agreed SLAs/SLOs. * Represent Advanced Analytics in architecture, security, and governance forums to ensure compliant deployment patterns. * Continuously improve turnaround times, stability, and observability of ML services; proactively communicate incidents and remediation. * Champion solution explainability and auditability to support business adoption and regulatory expectations. PEOPLE * Establish and socialise engineering standards for model packaging, testing, observability, and documentation. * Operate effectively in a lean, business\-embedded advanced analytics team, balancing tactical delivery with the progressive maturation of engineering practices. * Coach actuaries and data scientists on production\-ready coding practices (data contracts, dependency management, testing, reproducibility). * Contribute to a culture of continuous improvement and incident learning (post\-mortems, blameless RCA, playbooks). * Participate in a positive work climate and culture to energise employees and give meaning to work. * Promote a culture that guides and directs best practice, fostering an environment of continuous learning, improvement and cohesiveness. * Participate in a learning and growth culture whereby information regarding successes, issues, trends and ideas are actively shared within Momentum Corporate but also at Group level, through participation in Group\-wide Forums * Participate in performance management frameworks in line with Momentum Corporate’s guidelines. * Encourage innovation, change agility and collaboration within the team. FINANCE * Design for cost efficiency across compute, storage, and orchestration; monitor and optimise runtime costs of pipelines and model endpoints. * Implement controls that reduce operational and compliance risk (access, secrets, data minimisation, retention). * Track and report service reliability metrics (uptime, latency, failure rates) and cost KPIs relevant to production ML operations. * Optimally contribute to the budget design for the area, including the motivation for expenditures and implementation of financial regulations. * Follow risk management, governance, and compliance policies in own practice area, to identify and manage risk exposure liability. Work Location: Cape Town, Western Cape, South Africa Availability: Full time

Job Title: LLM Infrastructure Engineer Company: Hire Hangar Job Category: Machine Learning Roles & Responsibilities: Role Overview: We are seeking an LLM Infrastructure Engineer with hands\-on experience building the systems that power large language models. This is a full\-time, hands\-on engineering role focused on designing, deploying, and scaling the infrastructure behind model training, fine\-tuning, and inference. You will work closely with machine learning and engineering teams to make sure models run reliably, efficiently, and cost\-effectively in production. The ideal candidate understands both the demands of LLM workloads and the cloud systems needed to support them at scale. Key Responsibilities: * Design, build, and maintain infrastructure for training, fine\-tuning, and serving large language models * Deploy and scale LLM inference services with a focus on latency, throughput, and reliability * Manage GPU compute resources and optimize workload scheduling and utilization * Build and maintain data pipelines that support model training and evaluation * Implement monitoring, logging, and alerting for model performance and system health * Optimize infrastructure costs while maintaining performance and availability * Automate deployment workflows using infrastructure\-as\-code and CI/CD practices * Collaborate with ML engineers and researchers to move models from experimentation into production * Proactively identify bottlenecks, investigate root causes, and resolve infrastructure issues Required Qualifications: * AWS * Python * Docker and Kubernetes * Terraform or CloudFormation * PyTorch and LLM serving frameworks * CI/CD and monitoring tools * Slack, Zoom, and other remote collaboration tools Please NOTE: Work Location: Johannesburg, GP, South Africa Availability: Full time

Job Title: AI/ML Engineer – AI Governance & Automation Company: Mantra Consultancy Services Job Category: Machine Learning Roles & Responsibilities: Key Responsibilities * Develop and implement AI/ML solutions using Python. * Build and maintain automated workflows, data pipelines, and API integrations. * Implement AI governance, Responsible AI, and data governance practices. * Ensure AI solutions align with applicable regulatory and governance frameworks. * Work with cross-functional teams to improve AI automation and data processing. * Support AI model development, validation, monitoring, and compliance. * Contribute to GenAI and Agentic AI initiatives where applicable. Work Location: In person Work Location: Karnataka, India Availability: Full time

Job Title: Senior Data Scientist Company: Ericsson Job Category: Machine Learning Roles & Responsibilities: Join our Team About the Opportunity: Ericsson Enterprise Wireless Solutions (BEWS) is responsible for driving Ericsson's Enterprise Networking and Security business. Our expanding product portfolio covers wide area networks, local area networks, and enterprise security. We are the #1 global market leader in Wireless-WAN enterprise connectivity and are rapidly growing in enterprise Private 5G networks and Secure Access Services Edge (SASE) solutions. What Will You Do: Lead the design and implementation of machine learning and statistical models to solve complex business problems across multiple domains. Collaborate with cross-functional teams - data engineers, product managers, and architects - to identify high-impact opportunities for AI-driven solutions and translate them into production-grade systems. Perform data exploration, feature engineering, and dataset curation to support both classical ML model development and LLM grounding/context pipelines. Stay at the forefront of AI/ML research - actively tracking arXiv, open-source releases, and industry developments Experience deploying and scaling AI models in production with robust monitoring, observability, and performance management. Work Location: Bengaluru, Karnataka, India Availability: Full time

Job Title: AI or ML Engineer Company: Optum Job Category: Machine Learning Roles & Responsibilities: Primary Responsibilities: * Apply structured, hypothesis-driven problem solving, with guidance, to translate well-defined business questions into analytical approaches * Analyze healthcare data from one or a limited number of sources to identify trends and opportunities for improvement across cost, quality, and growth * Summarize findings into clear, decision-ready insights with a quantified business impact, typically reviewed by a senior analyst before distribution * Support stakeholders in a matrixed COE environment by gathering requirements, documenting problem statements, and helping align on priorities * Communicate insights clearly to operational audiences; assist senior analysts in preparing executive-facing storytelling * Help identify and flag opportunities for optimization for review and prioritization by senior team members * Support analytical delivery tasks such as data exploration, feature engineering, and results interpretation under supervision * Collaborate with data engineering and data platform teams to support data availability, quality, and usability for assigned use cases * Contribute to reusable analytical assets, frameworks, and best practices as directed by senior team members * Ensure analytical outputs are accurate and reliable, with clearly documented assumptions and limitations * Support process improvements to reduce low-value or redundant work * Assist in educating stakeholders on data-driven decision making and evidence-based practices * Works with well-defined, moderately complex issues * Developing as a resource to others within the immediate team * Comply with all applicable Company policies, procedures, and business directives, changes including those relating to work location, team assignments, work schedules, and flexible work arrangements Work Location: Gurugram, India Availability: Full time

Job Title: Senior Data Engineer Company: Cubic Corporation Job Category: Machine Learning Roles & Responsibilities: The Senior Data Engineer will play a key role in designing, building, and maintaining scalable data pipelines, integrations, and analytical platforms that support CTS’s global analytics and reporting platforms. This role is responsible for developing high-quality ETL/ELT processes, managing cloud data environments, ensuring data quality, and enabling advanced analytics and reporting across major programs. You will work extensively with Azure Data Factory, Azure Databricks, Python, SQL (Oracle/SQL Server), data modelling, data lakes, and enterprise data warehouse structures to support KPI engines, device analytics, predictive modelling, and operational performance insights. ### Key Responsibilities The following are the key responsibilities of the position. It is expected that most, if not all of these, are met by the candidate: * Design, build, and optimize data pipelines using Azure Data Factory and Databricks (pyspark/spark SQL). * Develop robust ETL/ELT processes to ingest, transform, and validate large volumes of operational, telemetry, incident, and transactional data. * Implement scalable workflows leveraging Azure services such as Data Lake Storage, Azure Synapse, Azure SQL Databases, Key Vault, Logic Apps, and Functions. * Build efficient advanced SQL queries and stored procedures across Azure Synapse and Azure SQL Server. * Collaborate with data analysts, engineers, performance assurance, and operations teams to enable reliable, accurate datasets for reporting, KPIs and predictive analytics. * Design and maintain data models, schemas, tables, and metadata following COE architecture patterns. * Implement strong data quality, validation, and monitoring frameworks to ensure accuracy and reliability across global programs. * Strong analytical mindset with exceptional problem-solving abilities. * Ability to work independently and take ownership of deliverables. * Excellent communication skills-capable of simplifying complex concepts for non-technical stakeholders. * Comfortable working in a fast-paced, global, multi-time zone team. * High attention to detail, data quality, and accuracy. * Strong sense of accountability, adaptability, and continuous improvement. * Ability to engage effectively with engineering, operations, and business stakeholders. * Proactive, resourceful, and committed to delivering high-quality outcomes. Role Impact & Success Measures ---------------------------------- The Senior Data Engineer will design, build, and maintain enterprise data warehouse solutions for analytics and reporting, develop and optimize ETL/ELT pipelines to load and transform data from multiple sources. Implement dimensional modelling (star/snowflake schemas) for performance and scalability, and ensure data quality, governance, and performance tuning across the warehouse whilst strengthening solutions using Lakehouse, Data Warehouse, and Real-Time Analytics. * Build a solid understanding of CTS datasets, KPI frameworks, and key operational systems. * Gain strong understanding of CTS datasets, pipelines, and Azure architecture. * Deliver reliable ADF and Databricks pipelines for ingestion and transformation. * Improve data quality and performance of existing workflows. * Support analysts and operations teams with accurate, well-structured datasets. * Take ownership of end-to-end analytical workstreams with minimal supervision. * Own end-to-end pipeline development with minimal supervision. * Implement scalable, reusable engineering patterns and metadata-driven frameworks. * Improve efficiency through automation, orchestration, and optimization. * Integrate new data sources to support KPIs, device analytics, and predictive modelling. * Contribute to CTS Analytics strategy, standards, and global operating model improvements. * Contribute to global COE standards for data engineering and cloud architecture. * Become SME for key pipelines, data domains, or Azure components. * Lead continuous improvement initiatives around quality, governance, and automation. Support junior engineers and uplift engineering capability within the COE. Work Location: Hyderabad, India Availability: Full time

Job Title: Senior Data Engineer Company: Cubic Corporation Job Category: Machine Learning Roles & Responsibilities: The Senior Data Engineer will play a key role in designing, building, and maintaining scalable data pipelines, integrations, and analytical platforms that support CTS’s global analytics and reporting platforms. This role is responsible for developing high-quality ETL/ELT processes, managing cloud data environments, ensuring data quality, and enabling advanced analytics and reporting across major programs. You will work extensively with Azure Data Factory, Azure Databricks, Python, SQL (Oracle/SQL Server), data modelling, data lakes, and enterprise data warehouse structures to support KPI engines, device analytics, predictive modelling, and operational performance insights. ### Key Responsibilities The following are the key responsibilities of the position. It is expected that most, if not all of these, are met by the candidate: * Design, build, and optimize data pipelines using Azure Data Factory and Databricks (pyspark/spark SQL). * Develop robust ETL/ELT processes to ingest, transform, and validate large volumes of operational, telemetry, incident, and transactional data. * Implement scalable workflows leveraging Azure services such as Data Lake Storage, Azure Synapse, Azure SQL Databases, Key Vault, Logic Apps, and Functions. * Build efficient advanced SQL queries and stored procedures across Azure Synapse and Azure SQL Server. * Collaborate with data analysts, engineers, performance assurance, and operations teams to enable reliable, accurate datasets for reporting, KPIs and predictive analytics. * Design and maintain data models, schemas, tables, and metadata following COE architecture patterns. * Implement strong data quality, validation, and monitoring frameworks to ensure accuracy and reliability across global programs. * Strong analytical mindset with exceptional problem-solving abilities. * Ability to work independently and take ownership of deliverables. * Excellent communication skills-capable of simplifying complex concepts for non-technical stakeholders. * Comfortable working in a fast-paced, global, multi-time zone team. * High attention to detail, data quality, and accuracy. * Strong sense of accountability, adaptability, and continuous improvement. * Ability to engage effectively with engineering, operations, and business stakeholders. * Proactive, resourceful, and committed to delivering high-quality outcomes. Role Impact & Success Measures ---------------------------------- The Senior Data Engineer will design, build, and maintain enterprise data warehouse solutions for analytics and reporting, develop and optimize ETL/ELT pipelines to load and transform data from multiple sources. Implement dimensional modelling (star/snowflake schemas) for performance and scalability, and ensure data quality, governance, and performance tuning across the warehouse whilst strengthening solutions using Lakehouse, Data Warehouse, and Real-Time Analytics. * Build a solid understanding of CTS datasets, KPI frameworks, and key operational systems. * Gain strong understanding of CTS datasets, pipelines, and Azure architecture. * Deliver reliable ADF and Databricks pipelines for ingestion and transformation. * Improve data quality and performance of existing workflows. * Support analysts and operations teams with accurate, well-structured datasets. * Take ownership of end-to-end analytical workstreams with minimal supervision. * Own end-to-end pipeline development with minimal supervision. * Implement scalable, reusable engineering patterns and metadata-driven frameworks. * Improve efficiency through automation, orchestration, and optimization. * Integrate new data sources to support KPIs, device analytics, and predictive modelling. * Contribute to CTS Analytics strategy, standards, and global operating model improvements. * Contribute to global COE standards for data engineering and cloud architecture. * Become SME for key pipelines, data domains, or Azure components. * Lead continuous improvement initiatives around quality, governance, and automation. Support junior engineers and uplift engineering capability within the COE. Work Location: Telangana, India Availability: Full time