MLOps / LLMOps Engineer - Deloitte

Responsible for designing, building, and maintaining end‑to‑end MLOps and LLMOps pipelines to support model lifecycle management across cloud platforms. The role focuses on model evaluation, drift monitoring, CI/CD, deployment, observability, and rollback procedures, while integrating with data engineering and quality engineering practices.

• Design and implement MLOps/LLMOps pipelines using MLflow and CI/CD tools
• Develop and maintain model evaluation, RAG/agent evaluation, drift monitoring, and rollback mechanisms
• Build, containerize, and deploy models with Docker, Kubernetes/AKS, and Azure Functions
• Create and maintain REST APIs using FastAPI/Flask for model inference
• Ensure data quality and integrity using SQL, Pandas, NumPy, and PySpark
• Integrate cloud services (AWS, Azure) for storage, compute, and monitoring
• Collaborate with data scientists and software engineers to streamline model deployment
• Automate testing and quality assurance with PyTest and Selenium

• 5-8 years of experience in MLOps/LLMOps
• Proficiency in Python, SQL, PySpark, and data engineering tools
• Hands‑on experience with MLflow, CI/CD pipelines, and cloud services (AWS, Azure)
• Strong knowledge of Docker, Kubernetes/AKS, and container orchestration
• Experience with Git, GitHub Actions, and Jenkins
• Familiarity with FastAPI/Flask, REST APIs, and JSON handling
• Experience in model evaluation, RAG/agent evaluation, drift monitoring, and rollback strategies
• Quality engineering skills using PyTest and Selenium