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ML Tech Lead (GenAI)
Job Description
Provectus helps companies adopt ML/AI to transform the ways they operate, compete, and drive value. The focus of the company is on building ML Infrastructure to drive end-to-end AI transformations, assisting businesses in adopting the right AI use cases, and scaling their AI initiatives organization-wide in such industries as Healthcare & Life Sciences, Retail & CPG, Media & Entertainment, Manufacturing, and Internet businesses.
We are seeking a highly skilled GenAI Tech Lead with a strong background in Large Language Models (LLMs) and AWS Cloud services. The ideal candidate will oversee the development and deployment of cutting-edge AI solutions while managing a team of engineers. This leadership role demands hands-on technical expertise, strategic planning, and team management capabilities to deliver innovative products at scale.
Core Responsibilities:
- 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:
- 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
What We Offer:
Interview stages:
