ML Ops

Roles and Responsibilities

• Build and deploy production-grade ML pipelines for varied use cases across operations, manufacturing, supply chain, and more 
• Work hands-on in designing, training, and fine-tuning models across traditional ML, deep learning, and GenAI (LLMs, diffusion models, etc.) 
• Collaborate with data scientists to transform exploratory notebooks into scalable, maintainable, and monitored deployments 
• Implement CI/CD pipelines, version control, and experiment tracking using tools like MLflow, DVC, or similar 
• Do shadow deployment and A/B testing of production models  
• Partner with data engineers to build data pipelines that support real-time or batch model inference 
• Ensure high availability, performance, and observability of deployed ML solutions using MLOps best practices 
• Conduct code reviews, performance tuning, and contribute to ML infrastructure improvements 
• Support the end-to-end lifecycle of ML products 
• Contribute to knowledge sharing, reusable component development, and internal upskilling initiatives

Experience & Background

• 4-6 years of hands-on experience in developing & deploying ML models
• Significant exposure to MLOps practices and end-to-end ML lifecycle
• Experience in production-grade deployments (not just notebooks or POCs)
• Exposure to at least 2 domains

Technical Skills

• Strong Python coding skills
• Experience with ML/DL libraries: scikit-learn, TensorFlow, PyTorch
• Familiar with CI/CD, experiment tracking (MLflow, DVC)
• Worked with tools like Docker, Kubernetes, FastAPI/Flask, Airflow, Git
• Cloud exposure: Azure or AWS (at least one)

Education

• Bachelor's in CS, Engineering, Data Science, or related field