IND Lead Software Engineer

About this role

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Skills you'll use

What you'll do

• Design and implement production-grade AI/ML and Agentic AI solutions that drive end-to-end transformation across pricing, underwriting, and sales.
• Partner with Cloud, AIOps, Data Science, LOB IT, Enterprise Architecture, and Data teams to provision infrastructure, deploy services, and operate scalable AI platforms using modern DevOps practices.
• Leverage AI Platform, agent development standards, and agent frameworks to build, deploy, monitor and maintain agentic solutions & AI/ML pipelines.
• Architect and build highly available, scalable, secure, and fault-tolerant AI/ML systems, applying modern distributed system patterns such as event-driven, pub/sub, and point-to-point architectures.
• Design and implement agent memory, evaluation, and feedback mechanisms to enable quality, safety, and reliability-driven tuning and continuous improvement.
• Develop advanced context engineering, adaptive prompting, multi-agent coordination, and RAG/Agentic RAG systems using techniques such as HyDE, RAPTOR, and GraphRAG to improve accuracy and relevance.
• Write high-quality, production-ready Python (e.g., asyncio, FastAPI, Pydantic) and instrument AI observability using OpenTelemetry, offline evaluation, and drift monitoring, while leveraging enterprise AI platforms and standards.
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Who can apply

• Experience Range - 4 to 6 Years
• Bachelor’s or Master’s degree in computer science , Software Engineering, Data Science, or a closely related discipline.
• Professional experience in ML, Software Engineering, or a related role, including 3+ years delivering AI/ML solutions in production.
• Strong Python development experience, building and operating production services and APIs.
• Generative AI & Agentic Systems
• Experience developing full-stack agentic solutions using agent frameworks such as ADK, A2A, MCP, LangChain, LangGraph, or CrewAI, and familiarity with commercial and open-source foundation models.
• Experience building and operating advanced RAG and Agentic RAG systems using modern techniques and methodologies.
• Experience with agentic monitoring, observability, and model evaluation frameworks to assess quality, safety, and performance in production.
• ML, Platforms & Cloud
• Hands-on experience with ML and AI frameworks such as PyTorch, Hugging Face, Pandas, NumPy, and related libraries.

About The Hartford

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