Job Description

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.