Provectus
Healthcare & Life Sciences, Retail & CPG, Media & Entertainment, Manufacturing, and Internet businesses
MLTechLead(GenAI,AWS)
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optimal for Senior candidates.
“ML Tech Lead (GenAI, AWS) at Provectus. Skills: GenAI, LLMs, AWS, ML Engineering, Technical Leadership. Set technical direction and standards for ML projects. Make architectural decisions for ML systems”
What You'll Achieve.
Deliver innovative products at scale; Drive end-to-end AI transformations; Assist businesses in adopting the right AI use cases; Scale their AI initiatives organization-wide
Industry & Context.
Troubleshoot complex technical challenges; Help engineers debug complex issues; Tackle highest-risk technical challenges
What They're Looking For.
Must Have
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: understanding of ML infrastructure and operations, LLM Systems: Experience with modern LLM-based applications and RAG, Code Quality: Exemplary coding standards and best practices, 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, 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
Nice to Have
Kubernetes a plus
What You'll Do.
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
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
How You'll Work.
Team & Collaboration
Manage a team of engineers; 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; Advanced Git workflows and collaboration patterns
Communication Scope
Share knowledge through workshops and documentation
Full Job Description
## Description Provectus helps companies adopt ML/AI to transform the ways they operate, compete, and drive value. The focus of the company is on building ML Infrastructure to drive end-to-end AI transformations, assisting businesses in adopting the right AI use cases, and scaling their AI initiatives organization-wide in such industries as Healthcare & Life Sciences, Retail & CPG, Media & Entertainment, Manufacturing, and Internet businesses. We are seeking a highly skilled GenAI Tech Lead with a strong background in Large Language Models (LLMs) and AWS Cloud services. The ideal candidate will oversee the development and deployment of cutting-edge AI solutions while managing a team of engineers. This leadership role demands hands-on technical expertise, strategic planning, and team management capabilities to deliver innovative products at scale. ## 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
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