Associate MLOps Engineer

Role Overview:

Support the deployment, scaling, optimization, and monitoring of AI/ML models in production environments. Work closely with data scientists and developers to ensure models run efficiently, reliably, and with fast inference performance.

Key Responsibilities

• Develop, maintain, and deploy ML/AI models into production environments.
• Build and serve model inference APIs using frameworks like FastAPI.
• Optimize models for better inference performance including quantization and model compression.
• Package and containerize models using Docker and manage deployments with orchestration tools (e.g., Kubernetes).
• Set up CI/CD pipelines and automation workflows for model deployment.
• Monitor model performance, latency, and reliability in production.
• Troubleshoot and resolve deployment, infrastructure, or inference issues.
• Collaborate with ML Engineers, Data Scientists, and DevOps teams to streamline workflows.

Skills & Requirements

• Proficiency in Python and familiarity with building REST APIs (FastAPI, Flask).
• Experience deploying ML models and serving them reliably.
• Understanding of model optimization techniques such as quantization for faster inference.
• Knowledge of Docker and container orchestration (e.g., Kubernetes).
• Familiarity with CI/CD tools and automation workflows.
• Ability to monitor and troubleshoot production systems.

Nice to Have

• Experience with model versioning and ML lifecycle tools.
• Exposure to any cloud platform (AWS, GCP, Azure).
• Understanding of performance profiling and benchmarking tools.