Job Title: MLOps Engineer Location: [Insert Location – e.g., Gurugram / Remote / On-site] Experience: 2–5 years Type: Full-Time Key Responsibilities: Design, develop, and maintain end-to-end MLOps pipelines for seamless deployment and monitoring of ML models. Implement and manage CI/CD workflows using modern tools (e.g., GitHub Actions, Azure DevOps, Jenkins). Orchestrate ML services using Kubernetes for scalable and reliable deployments. Develop and maintain FastAPI-based microservices to serve machine learning models via RESTful APIs. Collaborate with data scientists and ML engineers to productionize models in Azure and AWS cloud environments. Automate infrastructure provisioning and configuration using Infrastructure-as-Code (IaC) tools. Ensure observability, logging, monitoring, and model drift detection in deployed solutions. Required Skills: Strong proficiency in Kubernetes for container orchestration. Experience with CI/CD pipelines and tools like Jenkins, GitHub Actions, or Azure DevOps. Hands-on experience with FastAPI for developing ML-serving APIs. Proficient in deploying ML workflows on Azure and AWS . Knowledge of containerization (Docker optional, if used during local development). Familiarity with model versioning, reproducibility, and experiment tracking tools (e.g., MLflow, DVC). Strong scripting skills (Python, Bash). Preferred Qualifications: B.Tech/M.Tech in Computer Science, Data Engineering, or related fields. Experience with Terraform, Helm, or other IaC tools. Understanding of DevOps practices and security in ML workflows. Good communication skills and a collaborative mindset. Show more Show less