Lead AI Engineer

Brief Description Design and build end-to-end AI systems that power real-world, production-grade use cases across industries like healthcare, insurance, and commerce. Prototype and experiment - rapidly turn new ideas into functional proof-of-concepts that push the boundaries of human-centric automation. Evaluate and fine-tune models, from classical ML pipelines to LLM-based reasoning systems, optimizing for performance, explainability, and reliability. Develop rigorous evaluation frameworks to ensure model behavior, alignment, and performance meet production standards. Deploy monitoring and observability tools for AI systems - tracking anomalies, staleness, and drift to maintain model health over time. Design agentic workflows and dynamic context engines that enable adaptive, intent-aware automation for business processes. Lead and mentor a small team of engineers through the full lifecycle - from architecture and experimentation to deployment, monitoring, and iteration Preferred Skills 5+ years of professional experience in applied machine learning or AI systems development, with at least 2 years leading projects or small teams. Proven expertise in end-to-end model development, deployment, and iteration from research to production. Strong foundation in both deep learning and classical ML frameworks, with the ability to choose the right approach for the problem. Hands-on experience building LLM-powered applications, including fine-tuning, evaluation, and context engineering. Strong grasp of model evaluation, drift detection, and observability in production environments. Creative problem-solving mindset - comfortable generating or curating synthetic or weakly supervised data when needed. Experience designing or implementing agentic systems, reasoning pipelines, or AI orchestration layers is a strong plus. Ability to mentor engineers, collaborate across disciplines, and translate abstract AI ideas into reliable, human-aligned systems.