Generative AI Tech Lead

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 We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us. #J-18808-Ljbffr