Machinist and Developer

5 days ago

Gurgaon, Haryana, India beBeeMachine Full time ₹ 2,00,00,000 - ₹ 2,50,00,000
Machine Learning Operations Role

About the Position:

This is an exciting opportunity to join a leading renewable energy company and play a key role in designing, building, and deploying scalable machine learning solutions to drive business outcomes.

Key Responsibilities:

  • Design and deploy production-grade ML pipelines for various 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 Generative AI (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.

Eligibility Criteria:

  • Bachelor's degree in Computer Science, Engineering, Data Science, or related field. Masters degree preferred.
  • At least 4 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.

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