1 month ago Be among the first 25 applicantsAs 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.
While this is not a people-management role, you'll serve as the technical anchor and go-to expert for the team.Core Responsibilities1.
Technical Leadership (40%)Set technical direction and standards for ML projectsMake architectural decisions for ML systemsReview and approve technical designsIdentify and address technical debtChampion best practices in ML engineeringTroubleshoot complex technical challengesEvaluate and introduce new technologies and tools2.
Mentorship & Team Development (35%)Mentor junior and mid-level ML engineers (2-5 engineers)Conduct technical code reviewsProvide guidance on technical problem-solvingHelp engineers debug complex issuesCreate learning opportunities and growth pathsShare knowledge through workshops and documentationBuild technical competency across the team3.
Hands-On Technical Work (25%)Contribute code to critical or complex componentsBuild proof-of-concepts for new approachesTackle highest-risk technical challengesDevelop reusable ML accelerators and frameworksMaintain technical credibility through active codingRequirements1.
ML Engineering ExcellenceDeep ML Expertise: Advanced knowledge across multiple ML domainsProduction ML:



Extensive experience building production-grade ML systemsArchitecture: Ability to design scalable, maintainable ML architecturesMLOps: Strong understanding of ML infrastructure and operationsLLM Systems: Experience with modern LLM-based applications and RAGCode Quality: Exemplary coding standards and best practices2.
Technical BreadthMultiple ML Frameworks: Proficiency across TensorFlow, PyTorch, scikit-learnCloud Platforms: Advanced AWS experience, familiarity with othersData Engineering: Understanding of data pipelines and infrastructureSystem Design: Ability to design complex distributed systemsPerformance Optimization: Experience optimizing ML models and infrastructure3.
Software EngineeringClean Code: Writes exemplary, maintainable codeTesting: Champions testing practices (unit, integration, ML-specific)Git & Collaboration: Advanced Git workflows and collaboration patternsCI/CD: Experience building and maintaining ML pipelinesDocumentation: Creates clear, comprehensive technical documentationWe may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses.
These tools assist our recruitment team but do not replace human judgment.
Final hiring decisions are ultimately made by humans.
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📌 Ml Tech Lead (Wih Genai) (Medellín)
🏢 Provectus
📍 Medellín