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 Generative AI 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.
What We Offer:
- 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.
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.
