ProvectusAll openings at Provectus

ML Tech Lead (GenAI, AWS)

ColombiaFull-timeRemoteEngineering

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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
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