As an ML Tech Lead, you'll provide technical leadership, mentor engineers, guide technical decisions, and ensure code quality in ML projects.
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%)
- 2. Mentorship & Team Development (35%)
- 3. Hands-On Technical Work (25%)
- 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
- 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
- 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
- 2. Technical Breadth
- 3. Software Engineering
- 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
- 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
- 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
Top Skills
AWS
Ml
PyTorch
Scikit-Learn
TensorFlow
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