IND Lead Software Engineer

We’re determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals - and to help others accomplish theirs, too. Join our team as we help shape the future.

Position Overview

We are seeking an AI/ML Engineer who will be responsible for architecting, building and deploying production-grade AI systems This is a highly hands-on role requiring deep expertise in ML engineering, MLOps, LLM architecture, and Generative/Agentic AI concepts and tooling exposure. This role is well-suited for someone who brings intellectual curiosity, a bias toward action, and a collaborative mindset, and who is looking to deepen their AI/ML engineering expertise while taking on increasing responsibility over time________________________________________

Key Responsibilities

• 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. ________________________________________

Required Skills & Experience:

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.
• Hands-on experience with at least one public cloud AI/GenAI platform (e.g., AWS SageMaker/Bedrock or Google Vertex AI, Vertex AI Search, and RAG Engine). Software Engineering, DevOps & Security
• Experience designing and delivering production-grade APIs and microservices using modern software engineering practices.
• Hands-on experience with DevOps and CI/CD pipelines, infrastructure as code (e.g., Terraform), GitHub collaboration, and cloud deployments.
• Experience with DevSecOps tools such as Nexus, SonarQube, Checkmarx, and mcp-scan. Ways of Working & Communication
• Experience working in lean, agile environments (e.g., SAFe or similar frameworks).
• Strong communication and collaboration skills, with the ability to explain complex technical concepts to technical and non-technical stakeholders, influence decisions, and work effectively across teams. ________________________________________Nice to Have
• Knowledge of automated testing, validation gates, canary deployments, and rollback strategies for ML and Agentic AI systems.
• Experience designing and implementing data pipelines for ML and Agentic AI workloads using modern data platforms (e.g., Snowflake, Airflow, S3/Glue/EMR/Redshift, Apache Iceberg, or equivalent).
• Experience working in insurance or other regulatory environments.
• Ability to partner with governance, risk, compliance, and security teams to ensure responsible AI through techniques such as bias mitigation, disparate impact analysis, and counterfactual testing.

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