ML Tech Lead (GenAI)

Provectus; Denver (CO); On-site; USD 120,000 - 160,000; Full time

Job summary

A technology consultancy in Denver is seeking an ML Tech Lead to provide technical leadership and mentorship for the ML engineering team. In this role, you will set technical standards, guide architectural decisions, and mentor engineers to foster a culture of technical excellence. The ideal candidate possesses deep ML expertise and proficiency in key tools like TensorFlow and PyTorch. This role is critical for driving innovative ML solutions and maintaining high coding standards.

Qualifications

• Deep ML expertise across multiple domains.
• Experience with production-grade ML systems.
• Scalable architecture design and MLOps understanding.

Responsibilities

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

Skills

Deep ML expertise

Proficiency in TensorFlow

Proficiency in PyTorch

Proficiency in scikit-learn

Advanced AWS experience

Clean code practices

Tools

TensorFlow

PyTorch

scikit-learn

Job description

As an ML Tech Lead, you'll provide technical leadership and mentorship for our ML engineering team in Colombia. You'll guide technical decisions, ensure code quality, mentor engineers, and help build a culture of technical excellence. Although this role is not a people‑management position, 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; review and approve technical designs; address technical debt; champion best practices; troubleshoot complex 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 problem‑solving; help 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, maintainable architecture design; MLOps understanding; modern LLM‑based application experience; exemplary coding standards.
• Technical Breadth – Proficiency in TensorFlow, PyTorch, scikit‑learn; advanced AWS experience; data pipeline and infrastructure knowledge; distributed system design; ML model and infrastructure optimization.
• Software Engineering – Clean, maintainable code; unit, integration, ML‑specific testing practices; advanced Git workflows; CI/CD for ML pipelines; comprehensive technical documentation.

We may use artificial intelligence tools to support parts of the hiring process, but final hiring decisions are made by humans.