ML Tech Lead (GenAI, AWS)

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 [link removed] 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.

Originally posted on Himalayas