About project 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 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 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 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 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 Testing: Champions testing practices (unit, integration, ML‑specific) Git & Collaboration: Advanced Git workflows and collaboration patterns CI/CD: Experience building and maintaining ML pipelines What We Offer 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 HR Interview HM Interview We are waiting for you to become a part of our team! #J-18808-Ljbffr