ML Tech Lead, GenAI

Job not on LinkedIn

🕒 March 6

Apply Now

Find Similar Remote Jobs

Set Up Email Alert For New Jobs

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume

Provectus

WebsiteLinkedInAll Job Openings

501 - 1000 employees

Founded 2012

🤖 Artificial Intelligence

☁️ SaaS

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.

📋 Description

• Provide technical leadership and mentorship for ML engineering team • Guide technical decisions and ensure code quality • Mentor engineers and build culture of technical excellence • Serve as the technical anchor and go-to expert for the team

🎯 Requirements

• 1. 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 • - 2. 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 • - 3. 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