Provectus

ML Tech Lead – GenAI

Provectus

full-time

Posted on:

Location Type: Remote

Location: Remote • 🇨🇴 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

  • 1. 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
  • - 2. 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
  • - 3. 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

Applicant Tracking System Keywords

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

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