The Role
The ML Tech Lead supervises the development of AI solutions, providing technical leadership, mentoring engineers, and managing project delivery primarily using AWS and Large Language Models.
Summary Generated by Built In
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%)
- Mentorship & Team Development (35%)
- Hands-On Technical Work (25%)
- 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
Requirements:
- ML Engineering Excellence
- Technical Breadth
- Software Engineering
- 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
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.
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The Company
What We Do
Provectus is an Artificial Intelligence consultancy and solutions provider, helping businesses achieve their objectives through AI. We are recognized by industry think tanks as a leading provider of AI solutions in specific business domains, driven by sophisticated IT service management and tech innovation. Provectus is a value driver and a trusted partner for our clients and employees. Provectus is an AWS Premier Consulting Partner with competencies in Data & Analytics, DevOps, and Machine Learning. We design and build AI solutions for industry-specific use cases, Data and Machine Learning foundation, Cloud transformation, and DevOps adoption.
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