Lead Machine Learning Engineer / Machine Learning Engineer

Lead Machine Learning Engineer / Machine Learning Engineer

Bangalore

7 - 11 Years

Responsibilities

• Lead and own the end‐to‐end production lifecycle of ML and LLM models (must have), ensuring models are deployable, scalable, observable, and maintainable.
• Define and enforce ML engineering and MLOps standards across teams (must have).
• Design and maintain CI/CD pipelines for ML workloads (must have).
• Act as technical lead and mentor for ML engineers and contributors (must have).
• Partner with Data Scientists to industrialize research into production systems (must have).
• Collaborate with Platform, Cloud, and Data Engineering teams on infrastructure and runtime alignment (must have).
• Own model monitoring, drift detection, testing, rollback, and incident analysis (must have).
• Evaluate and introduce new ML, GenAI, and MLOps tools with a pragmatic, enterprise mindset (good to have).
• Contribute to ML governance, reproducibility, and responsible AI practices (good to have).

Key Skills & Expertise

• Cloud & DevOps: Azure (must have), CI/CD using Jenkins, GitHub Actions, ArgoCD (must have)
• Container & Orchestration: Docker, Kubernetes (must have)5
• Workflow Orchestration: Airflow (must have)
• Programming: Production‐grade Python (must have)
• ML Engineering & GenAI: LLM integration , prompt engineering, model packaging and lifecycle management (must have)
• Testing & Quality: Pytest , integration and system testing for ML systems (must have)
• Data: SQL- 3+, relational databases, basic reporting and dashboards (must have)
• ML Platforms: MLflow, Databricks (good to have)
• LLM Frameworks: LangChain, LangGraph, agent‐based patterns (good to have) -
• Data Science Awareness: ML algorithms, feature engineering, evaluation metrics, bias/leakage awareness (awareness required)
• Specialized Use Cases: OCR and document processing pipelines (good to have)
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