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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.
Job Responsibility:
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
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
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
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
Requirements:
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
Nice to have:
Experience with Bedrock Agents, RAG pipelines, agentic workflows, or vector search
What we offer:
Sing-up bonus
10% Annual bonus
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
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