ML Tech Lead

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November 14

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Provectus

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Artificial Intelligence • SaaS

Provectus is an artificial intelligence consultancy and solutions provider that helps businesses transform through AI. Offering both a use case and a platform approach, Provectus integrates AI into organizations to achieve unique business objectives and technical capabilities. Their solutions are cloud-native, vendor-agnostic, and open, allowing for deployment in customer's cloud without restrictive licenses. With applications in industries like retail, manufacturing, and healthcare, Provectus delivers AI-powered use cases and turnkey solutions to drive innovation and efficiency. They also offer consulting, customization, and managed AI services.

501 - 1000 employees

Founded 2012

🤖 Artificial Intelligence

☁️ SaaS

📋 Description

• 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: 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 • 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 • 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