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 ManagementLead, mentor, and grow a team of 5–10 ML, Data, and Software EngineersDefine and drive the technical roadmap for ML/AI initiativesFoster a high-performance culture focused on ownership, learning, and engineering excellenceWork closely with Product, Data, and Platform teams to deliver end-to-end AI systemsMachine Learning & LLM EngineeringDesign, fine-tune, and deploy LLMs and ML models for real production use casesBuild systems for RAG, summarization, text generation, entity extraction, and other NLP/LLM workflowsExplore and implement emerging GenAI/LLM techniques and infrastructureContribute across the ML stack: NLP, deep learning, CV, RL, and classical MLAWS Cloud Architecture & MLOpsArchitect 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 latencyImplement MLOps/LLMOps best practices, CI/CD pipelines, monitoring, and automationEnsure security, reliability, observability, and compliance across ML workloadsTechnical Execution & Delivery ExcellenceLead the full ML lifecycle: research - experimentation - prototyping - production - maintenancePerform code reviews, lead architecture discussions, and ensure engineering best practicesTroubleshoot and optimize production ML systemsCommunicate project status, risks, and decisions to stakeholders and leadership Qualifications 5+ years of hands-on experience in Machine Learning, Deep Learning, or NLP2+ years in a technical leadership or team lead roleStrong expertise with LLMs (Hugging Face, OpenAI, Anthropic) and modern NLP stacksStrong hands-on experience with AWS ML ecosystem (SageMaker, Bedrock, Lambda, S3, ECS/ECR)Excellent Python engineering skills and proficiency with PyTorch or TensorFlowExperience building ML systems in production, not just researchSolid knowledge of MLOps/LLMOps tools, pipelines, and deployment best practicesStrong architectural thinking and ability to design scalable ML systemsExcellent communication skills and ability to lead cross-functional teamsPassion for mentoring engineers and raising the technical barExperience with Bedrock Agents, RAG pipelines, agentic workflows, or vector search What We Offer Sing-up bonus10% Annual bonus Long-term B2B collaborationFully remote setupComprehensive private medical insuranceor budget for your medical needs.Paid sick leave, vacation, and public holidaysContinuous learning support, including unlimited AWS certification sponsorship
