Provectus

ML Tech Lead – GenAI, AWS

Provectus

full-time

Posted on:

Location Type: Remote

Location: Colombia

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Job Level

Senior

Tech Stack

AWSCloudDistributed SystemsPyTorchScikit-LearnTensorflow

About the role

  • 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

  • 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
Benefits
  • 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.
Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools
ML engineeringproduction ML systemsML architectureMLOpsLLM systemsTensorFlowPyTorchscikit-learnAWSdata pipelines
Soft Skills
technical leadershipmentorshipproblem-solvingcommunicationteam developmenttechnical reviewsknowledge sharingdebuggingcollaborationcreating learning opportunities