Machine Learning Operations Engineer
On-site · Pune, Maharashtra, India
Job Summary
Design, build, and deploy robust ML pipelines for training, fine-tuning, and inference of models (NLP-focused: NER, Classification). Develop and maintain CI/CD workflows for ML pipelines using Jenkins or similar tools, ensuring rapid and safe deployment to production. Implement model monitoring and alerting systems to track performance degradation and drift in real-time. Collaborate with cross-functional teams to retrain models on trigger events and integrate feedback loops into the ML lifecycle. Hands on with Helm deployment of ML Pipelines in Kubernetes cluster and optimize for scalable and resilient operations. Use MLflow, Kubeflow, and related tools for experiment tracking, model versioning, and reproducibility. Write clean, efficient, and scalable code in Python using frameworks such as PyTorch and CUDA. Experience with tuning, optimising LLM Applications performance in production.
Required Qualifications
- 2–3 years of experience in building and deploying end-to-end ML products in production environments
- Strong programming experience in Python and PyTorch
- Hands-on experience with CI/CD pipelines using Jenkins
- Proficient with Kubernetes for deploying and managing ML workloads
- Experience with model training, fine-tuning, and inference pipeline development
- Working knowledge of model monitoring and alerting systems (performance drift, latency, accuracy drop)
- Experience with MLflow, Kubeflow, and model versioning best practices
- Solid understanding of NER, Text Classification, and common NLP tasks
- Familiarity with CUDA for training models on GPU
- Hands-on with Helm deployment of ML Pipelines in Kubernetes cluster
- Experience with Generative AI systems in production (nice to have)
- Prior experience with building or deploying applications in hardwares such as L40S, H100, H200 (nice to have)
- Familiarity with LangChain, LangGraph, LangSmith for building LLM-powered agents and applications (nice to have)
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