AI Lead

Overview

Position Overview: We are seeking an exceptionally talented and highly operational AI engineering Lead to spearhead our Intelligence, RAG & Context stream. You will lead hands-on, write core Agentic AI code, architect production-grade pipelines, and mentor a team of AI and Data Engineers to deliver high accuracy and explainable AI-based solutions. The ideal candidate has experience transitioning from traditional Machine Learning to LLMs, Advanced RAG, and Agentic AI systems, with a focus on the healthcare insurance market. Your mission is to turn raw cognitive power into enterprise-grade SaaS features and to lead the development of AI capabilities that power the claims management process, from data entry and enrichment to fraud detection and adjudication, providing claims handlers with relevant, accurate insights to accelerate decision-making.

Key Missions & Responsibilities

• Operational & Architectural Leadership: Architect, code, and deploy multi-agent systems and deep learning models for the Claims Data Platform. Own the technical delivery of your stream.
• Bridge the Paradigm Shift: Combine traditional ML for deterministic tasks (e.g., OCR/ICR parsing) and rule-based systems with GenAI/Agentic architectures (LangGraph, SmolAgents, MCP) for complex hybrid reasoning and fraud detection.
• R&D Industrialization: Ensure all AI models are wrapped with high-performance, API-first contracts for seamless integration with the product development team.
• Team Mentorship: Foster a high-ownership engineering culture. Mentor and develop Data Scientists, AI Engineers, and Back-End Data Engineers through code reviews, pairing, and architectural guidance.
• Evaluation & LLMOps: Establish rigorous evaluation frameworks. Build automated benchmarking pipelines to track model drift, cost-efficiency, latency, and token optimization before production release.

Required Experience & Qualifications

• Education: Master’s degree or PhD from a top-tier engineering school or university, specialized in Computer Science, AI, or Applied Mathematics.
• Experience: 7+ years of hands-on experience in applied AI/ML development, with a proven track record of shipping models to production.
• The GPT Pivot: Demonstrated success transitioning from heavy custom model training to mastering Foundation Models orchestration, advanced prompting, and tool use.
• Healthcare or Regulated Industry: Experience in regulated industries is a plus, especially with model explainability, data privacy, and secure handling of sensitive data.
• Technical Stack: Deep mastery of LLMs, Python, and large-scale data processing pipelines. Hands-on experience with agentic frameworks (e.g., LangGraph, SmolAgents, Swarm) and the Model Context Protocol (MCP). Strong LLMOps tooling (e.g., MLflow, Langfuse, Langsmith) for tracing, scoring, and benchmarking autonomous agents. Knowledge of vector databases, advanced RAG patterns, and knowledge graphs.

Soft Skills & Mindset

• Product-Minded & Business-Driven: Focus on user impact and business value, delivering strong production accuracy.
• High Ownership: Thrive in lean, high-performing environments with a small team of A-players.
• Communication: Ability to translate complex probabilistic behaviors into clear, structured insights for non-technical stakeholders (Product, Sales, Clients).