ML Tech Lead (wih GenAI)

Provectus; Cali; Híbrido; COP 230.034.888 - 306.713.184; Jornada completa

Descripción de la vacante

A leading technology company in Colombia seeks an experienced ML Tech Lead to provide technical leadership and mentorship for its ML engineering team. Responsibilities include setting technical direction, mentoring engineers, and contributing hands-on to complex projects. Candidates should have deep expertise in machine learning and proficiency in tools like TensorFlow and PyTorch. This role offers a chance to shape the technical landscape and drive innovation within a growing team.

Formación

• Deep ML expertise across multiple domains.
• Experience with modern LLM-based applications and RAG.
• Advanced AWS experience and familiarity with other clouds.

Responsabilidades

• Set technical direction and standards for ML projects.
• Mentor junior and mid-level ML engineers.
• Contribute code to critical or complex components.

Conocimientos

Deep ML expertise

Production-grade ML systems experience

Proficiency across TensorFlow

Proficiency across PyTorch

Proficiency across scikit-learn

Clean, maintainable code style

Testing expertise

Advanced Git workflows

MLOps knowledge

Herramientas

AWS

CI/CD pipelines

Descripción del empleo

3 weeks ago – Be among the first 25 applicants.

As an ML Tech Lead you will provide technical leadership and mentorship for our ML engineering team in Colombia. You will guide technical decisions, ensure code quality, mentor engineers, and help build a culture of technical excellence. While this is not a people‑management role, you will serve as the technical anchor and go‑to expert for the team.

Core 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 across multiple domains.
  Production‑grade ML systems experience.
  Scalable and maintainable ML architecture design.
  MLOps knowledge and infrastructure operations.
  Experience with modern LLM‑based applications and RAG.
  Exemplary coding standards and best practices.
• Technical Breadth
  Proficiency across TensorFlow, PyTorch, scikit‑learn.
  Advanced AWS experience and familiarity with other clouds.
  Data engineering and pipeline knowledge.
  Distributed system design.
  Performance optimization of ML models and infrastructure.
• Software Engineering
  Clean, maintainable code style.
  Testing expertise (unit, integration, ML‑specific).
  Advanced Git workflows and collaboration patterns.
  CI/CD pipeline building and maintenance.
  Clear, comprehensive technical documentation.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analysing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgement. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

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