Assistant Manager - Data Scientist (Machine Learning Operations)

Job Description

Role & 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 Preferred candidate profile Bachelor's in Computer Science, Engineering, Data Science, or related field. Masters degree preferred 4–6 years of experience in developing and deploying machine learning models, with significant exposure to MLOps practices Experience in implementing and productionizing Generative AI applications using LLMs (e.g., OpenAI, HuggingFace, LangChain, RAG architectures) Strong programming skills in Python; familiarity with ML libraries such as scikit-learn, TensorFlow, PyTorch Hands-on experience with tools like MLflow, Docker, Kubernetes, FastAPI/Flask, Airflow, Git, and cloud platforms (Azure/AWS) Solid understanding of software engineering fundamentals and DevOps/MLOps workflows Exposure to at least 2-3 industry domains (energy, manufacturing, finance, etc.) preferred Excellent problem-solving skills, ownership mindset, and ability to work in agile cross-functional teams

More Info

Education