**Machine Learning Tech Lead (Generative AI)** ======================== Colombia, Medellín, Antioquia, Bogotá, Capital District, Cali, Valle del Cauca, Barranquilla, Bucaramanga, Santander, Bucaramanga Metropolitan Area **About the project** As an ML Tech Lead, you'll provide technical leadership and mentorship for our ML engineering team in Colombia. You'll guide technical decisions, ensure code quality, mentor engineers, and help build a culture of technical excellence. While this is not a people-management role, you'll serve as the technical anchor and go-to expert for the team. **Core Responsibilities:** * 1. 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 * 2. 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 * 3. 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:** * 1. 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 * 2. 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 * 3. Software Engineering + Clean Code: Writes exemplary, maintainable code + Testing: Champions testing practices (unit, integration, ML-specific) + Git & Collaboration: Advanced Git workflows and collaboration patterns + CI/CD: Experience building and maintaining ML pipelines + Documentation: Creates clear, comprehensive technical documentation