Overview

We are seeking an AI/ML Lead Software Engineer 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. The 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 and 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 and Experience

• •Bachelor's or Master's degree in Computer Science, Software Engineering, Data Science, or a closely related discipline.
• •4 to 6 years of 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.
• •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.
• •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 such as AWS SageMaker/Bedrock or Google Vertex AI, Vertex AI Search, and RAG Engine.
• •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.
• •Experience working in lean, agile environments such as SAFe or similar frameworks.
• •Strong communication and collaboration skills, with the ability to explain complex technical concepts to technical and non-technical stakeholders.

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 such as 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.