MLTechLead(GenAI,AWS)

## Responsibilities Technical Leadership (40%) - Set technical direction and standards for ML projects - Make architectural decisions for ML systems - Review and approve technical designs - Identify and address technical debt - Champion best practices in ML engineering - Troubleshoot complex technical challenges - Evaluate and introduce new technologies and tools Mentorship & Team Development (35%) - Mentor junior and mid-level ML engineers (2-5 engineers) - Conduct technical code reviews - Provide guidance on technical problem-solving - Help engineers debug complex issues - Create learning opportunities and growth paths - Share knowledge through workshops and documentation - Build technical competency across the team Hands-On Technical Work (25%) - Contribute code to critical or complex components - Build proof-of-concepts for new approaches - Tackle highest-risk technical challenges - Develop reusable ML accelerators and frameworks - Maintain technical credibility through active coding ## Requirements ML Engineering Excellence - Deep ML Expertise: Advanced knowledge across multiple ML domains - Production ML: Extensive experience building production-grade ML systems - Architecture: Ability to design scalable, maintainable ML architectures - MLOps: Strong understanding of ML infrastructure and operations - LLM Systems: Experience with modern LLM-based applications and RAG - Code Quality: Exemplary coding standards and best practices Technical Breadth - Multiple ML Frameworks: Proficiency across TensorFlow, PyTorch, scikit-learn - Cloud Platforms: Advanced AWS experience, familiarity with others - Data Engineering: Understanding of data pipelines and infrastructure - System Design: Ability to design complex distributed systems - Performance Optimization: Experience optimizing ML models and infrastructure Software Engineering - Clean Code: Writes exemplary, maintainable code - Testing: Champions testing practices (unit, integration, ML-specific) - Git & Collaboration