Provectus company

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: 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

What We Offer:

• Long-term B2B collaboration;
• Fully remote setup;
• A budget for your medical insurance;
• Paid sick leave, vacation, public holidays;
• Continuous learning support, including unlimited AWS certification sponsorship.

Interview stages:

• Recruitment Interview;
• Tech interview;
• HR Interview;
• HM Interview.