ML Tech Lead (GenAI, AWS)

About Provectus

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.

Responsibilities:

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:

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

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

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

What We Offer:

• 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.

Interview stages:

• Recruitment Interview;
• Tech interview;
• HR Interview;
• HM Interview.