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