Machine Learning Operations Engineer

Come work at a place where innovation and teamwork come together to support the most exciting missions in the world! We are looking for a highly motivated Machine Learning Operations Engineer with 2–3 years of experience in building and deploying end-to-end ML products in production environments. The ideal candidate has a strong ML background in Binary/ Multi class Classification, Recommendation Chatbot Applications and deploying training/inference pipelines, with hands-on experience in CI/CD, monitoring, and Kubernetes deployments.   Key Responsibilities:   · 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 Skills:   · 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.   Good to Have:   · Experience with Generative AI systems in production. · Prior experience with building or deploying applications in Hardwares such as L40S, H100, H200. · Familiarity with LangChain, LangGraph, LangSmith for building LLM-powered agents and applications.