ML Tech Lead, GenAI, AWS

This is a fully remote position, open to applicants in Colombia.

📋 Description

• Technical Leadership: Establish the technical direction and standards for machine learning projects.

• Technical Leadership: Make architectural decisions pertaining to ML systems.

• Technical Leadership: Review and endorse technical designs.

• Technical Leadership: Identify and mitigate technical debt.

• Technical Leadership: Advocate for best practices in ML engineering.

• Technical Leadership: Resolve intricate technical challenges.

• Technical Leadership: Assess and integrate new technologies and tools.

• Mentorship: Guide junior and mid-level ML engineers (2-5 engineers).

• Mentorship: Perform technical code reviews.

• Mentorship: Offer guidance on resolving technical problems.

• Mentorship: Assist engineers in debugging complex issues.

• Mentorship: Disseminate knowledge through workshops and documentation.

• Mentorship: Cultivate technical competency within the team.

• Hands-On Technical Work: Contribute code to essential or complex components.

• Hands-On Technical Work: Develop proof-of-concept projects for innovative approaches.

• Hands-On Technical Work: Maintain technical credibility through active coding.

⛳️ Requirements

• Deep ML Expertise: In-depth knowledge across various ML domains.

• Production ML: Significant experience in developing production-grade ML systems.

• Architecture: Capability to design scalable and maintainable ML architectures.

• MLOps: Solid understanding of ML infrastructure and operational practices.

• LLM Systems: Experience with contemporary LLM-based applications and retrieval-augmented generation (RAG).

• Code Quality: Adheres to exemplary coding standards and best practices.

• Multiple ML Frameworks: Proficient in TensorFlow, PyTorch, and scikit-learn.

• Cloud Platforms: Advanced experience with AWS and familiarity with other cloud services.

• Data Engineering: Comprehension of data pipelines and related infrastructure.

• System Design: Ability to architect complex distributed systems.

• Performance Optimization: Experienced in optimizing ML models and associated infrastructure.

• Clean Code: Produces exemplary and maintainable code.

• Testing: Advocates for rigorous testing practices (unit, integration, and ML-specific).

• Git & Collaboration: Proficient in advanced Git workflows and collaboration techniques.

• CI/CD: Experience in constructing and sustaining ML pipelines.

• Documentation: Produces clear and comprehensive technical documentation.

🏝️ Benefits

• Long-term B2B collaboration;

• Fully remote setup;

• A budget for your medical insurance;

• Paid sick leave, vacation, and public holidays;

• Continuous learning support, including unlimited AWS certification sponsorship.