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.\n
\n \n

Key Responsibilities:
\n

\n

· Design, build, and deploy robust ML pipelines for training, fine-tuning, and inference of models (NLP-focused: NER, Classification).
\n · Develop and maintain CI/CD workflows for ML pipelines using Jenkins or similar tools, ensuring rapid and safe deployment to production.
\n · Implement model monitoring and alerting systems to track performance degradation and drift in real-time.
\n · Collaborate with cross-functional teams to retrain models on trigger events and integrate feedback loops into the ML lifecycle.
\n · Hands on with Helm deployment of ML Pipelines in Kubernetes cluster and optimize for scalable and resilient operations.
\n · Use MLflow, Kubeflow, and related tools for experiment tracking, model versioning, and reproducibility.
\n · Write clean, efficient, and scalable code in Python using frameworks such as PyTorch and CUDA.
\n · Experience with tuning, optimising LLM Applications performance in production.
\n

\n

Required Skills:
\n

\n

· Strong programming experience in Python and PyTorch.
\n · Hands-on experience with CI/CD pipelines using Jenkins.
\n · Proficient with Kubernetes for deploying and managing ML workloads.
\n · Experience with model training, fine-tuning, and inference pipeline development.
\n · Working knowledge of model monitoring and alerting systems (performance drift, latency, accuracy drop).
\n · Experience with MLflow, Kubeflow, and model versioning best practices.
\n · Solid understanding of NER, Text Classification, and common NLP tasks.
\n · Familiarity with CUDA for training models on GPU.
\n

\n

Good to Have:
\n

\n

· Experience with Generative AI systems in production.
\n · Prior experience with building or deploying applications in Hardwares such as L40S, H100, H200.
\n · Familiarity with LangChain, LangGraph, LangSmith for building LLM-powered agents and applications.