Senior Data Scientist (ML Engineer) OVA5861

Senior ML Engineer (12-Month Contract) Location: Hybrid We are seeking a Senior Machine Learning Engineer for a 12-month contract to operationalize, scale, and manage production-grade ML, AI, and GenAI solutions. You will focus on building robust MLOps pipelines on Databricks , deploying AI Agents and RAG solutions, and hosting containerized models and custom microservices on Azure Kubernetes Service (AKS) . Responsibilities include: Databricks & MLOps Pipelines: Productionize, automate, and monitor ML pipelines, model serving, and workflows using Databricks, MLflow, and Mosaic AI with full CI/CD practices. GenAI & Agent Deployment: Build, deploy, and support enterprise GenAI applications, AI Agents, and Retrieval-Augmented Generation (RAG) solutions. Kubernetes & API Engineering: Deploy and optimize open-source AI models and custom REST microservices on Azure Kubernetes Service (AKS) using Docker and Kubernetes. Model Observability & Support: Monitor model drift, operational health, performance, and infrastructure costs in production; resolve technical issues across APIs and pipelines. Cross-Functional Collaboration: Partner with Data Scientists to transition prototypes into scalable, compliant enterprise assets. Requirements include: Education: Degree in Computer Science, Engineering, Mathematical Statistics, Actuarial Science, Econometrics, or a quantitative field. Platform & Containerization: Strong hands-on experience with Databricks (MLflow, Model Serving) and Azure Kubernetes Service (AKS) using Docker and Kubernetes . Core Tech Stack: High proficiency in Python , SQL , and REST API development. MLOps & CI/CD: Demonstrated experience building automated CI/CD deployment pipelines, model monitoring, and observability frameworks. GenAI & Production ML: Practical experience deploying machine learning models, LLMs, RAG architectures, and microservices into live enterprise environments.