Key skills
AWSCloudDistributed SystemsPyTorchScikit-LearnTensorflowMLLLMRAGTensorFlowscikit-learnMLOpsData EngineeringGitPerformance OptimizationCI/CDLeadershipCollaboration
About this role
Role Overview
- 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
- 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.
Tech Stack
- AWS
- Cloud
- Distributed Systems
- PyTorch
- Scikit-Learn
- Tensorflow
Benefits
- Long-term B2B collaboration;
- Fully remote setup;
- A budget for your medical insurance;
- Paid sick leave, vacation, public holidays;
- Continuous learning support, including unlimited AWS certification sponsorship.