ProvectusProvectus

ML Tech Lead (GenAI, AWS)

Added 6 days ago

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.