Generative AI Tech Lead (LLMs, MLOps, AWS)

Provectus is an AI-first consultancy that helps global enterprises adopt Machine Learning and Generative AI at scale. We build modern ML infrastructure, design end-to-end AI systems, and deliver solutions that transform the way companies operate across Healthcare & Life Sciences, Retail & CPG, Media, Manufacturing, and high-growth digital industries.

Our teams work on impactful, production-grade AI projects — from Intelligent Document Processing platforms, to Demand Forecasting and Inventory Optimization engines, AI-powered Customer 360 systems, and advanced Healthcare/BioTech ML applications. Each solution combines strong engineering, deep ML expertise, and cloud-native architectures.

We are now looking for an experienced Machine Learning Tech Lead to drive the development of large-scale AI systems, lead a team of 5–10 engineers, and shape our Generative AI and LLM initiatives. This role is ideal for someone who wants to own architecture decisions, push the boundaries of GenAI/LLM technologies, and guide engineers in solving complex real-world problems.

Responsibilities:
• Leadership & Team Management
• Lead, mentor, and grow a team of 5–10 ML, Data, and Software Engineers
• Define and drive the technical roadmap for ML/AI initiatives
• Foster a high-performance culture focused on ownership, learning, and engineering excellence
• Work closely with Product, Data, and Platform teams to deliver end-to-end AI systems
• Machine Learning & LLM Engineering
• Design, fine-tune, and deploy LLMs and ML models for real production use cases
• Build systems for RAG, summarization, text generation, entity extraction, and other NLP/LLM workflows
• Explore and implement emerging GenAI/LLM techniques and infrastructure
• Contribute across the ML stack: NLP, deep learning, CV, RL, and classical ML
• AWS Cloud Architecture & MLOps
• Architect and operate scalable ML/AI systems using AWS (SageMaker, Bedrock, Lambda, S3, ECS/ECR…)
• Optimize model training, inference pipelines, and data workflows for scale, cost, and latency
• Implement MLOps/LLMOps best practices, CI/CD pipelines, monitoring, and automation
• Ensure security, reliability, observability, and compliance across ML workloads
• Technical Execution & Delivery Excellence
• Lead the full ML lifecycle: research - experimentation - prototyping - production - maintenance
• Perform code reviews, lead architecture discussions, and ensure engineering best practices
• Troubleshoot and optimize production ML systems
• Communicate project status, risks, and decisions to stakeholders and leadership

Qualifications:
• 5+ years of hands-on experience in Machine Learning, Deep Learning, or NLP
• 2+ years in a technical leadership or team lead role
• Strong expertise with LLMs (Hugging Face, OpenAI, Anthropic) and modern NLP stacks
• Strong hands-on experience with AWS ML ecosystem (SageMaker, Bedrock, Lambda, S3, ECS/ECR)
• Excellent Python engineering skills and proficiency with PyTorch or TensorFlow
• Experience building ML systems in production, not just research
• Solid knowledge of MLOps/LLMOps tools, pipelines, and deployment best practices
• Strong architectural thinking and ability to design scalable ML systems
• Excellent communication skills and ability to lead cross-functional teams
• Passion for mentoring engineers and raising the technical bar
• Experience with Bedrock Agents, RAG pipelines, agentic workflows, or vector search

What We Offer:
• Sing-up bonus
• 10% Annual bonus
• Long-term B2B collaboration
• Fully remote setup
• Comprehensive private medical insurance or budget for your medical needs.
• Paid sick leave, vacation, and public holidays
• Continuous learning support, including unlimited AWS certification sponsorship