ML Tech Lead (GenAI)

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.

- 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

- 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