Sr AI Engineer - Platform Engineering

Senior Staff Software Engineer - IE07HE
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. This requisition hires Senior AI Engineers who will:
Design and deliver production‑grade Agentic AI systems using Google ADK, Anthropic MCP, Lang Graph/Lang Chain, and modern Agentic protocols. Build secure, scalable AI platform capabilities with strong engineering fundamentals in Python/Typescript, Terraform, and GCP. Enable enterprise adoption of AI by creating reusable frameworks, APIs, and platform capabilities aligned with engineering standards, compliance needs, and modern cloud patterns.

AI/Agentic System Architecture & Development Design and implement Agentic AI solutions using Google ADK, Lang Graph, Lang Chain, and Agent Engine. Build and extend agent harnesses, implementing the agent execution loop, tool‑call orchestration, dynamic prompt and context assembly, sub‑agent delegation, streaming, token‑budget management, and hook and guardrail enforcement. Engineer advanced Lang Chain and Lang Graph orchestration, including LCEL chains, stateful graphs, checkpointing, human‑in‑the‑loop workflows, memory, retrievers, callbacks, and Lang Smith tracing and evaluation.

Build advanced RAG and GraphRAG pipelines, vector retrieval systems, and knowledge‑graph–augmented reasoning. Implement MCP‑compliant agents with capability registration, secure tool invocation, memory storage, and session state management. Apply deep knowledge of Agentic Protocol design (ADK & MCP), such as:
Agent memory and conversation state Tool authorization Multi‑step workflows and orchestration Session boundary and identity controls Leverage AlloyDB and PostgreSQL/RDS for:
Vector storage and hybrid search Agent memory persistence, session management, and state recovery Structured prompt scaffolding and fact retrieval ACID‑compliant transactional reasoning layers Develop scalable AI microservices using Python/Typescript, Cloud Run, Vertex AI, and event‑driven components. Optimize model inference, retrieval latency, and overall system performance.

Spec‑Driven & Agentic Development Drive spec‑driven development (SDD) using frameworks such as Git Hub Spec‑Kit, Open Spec, and BMAD‑METHOD, translating product intent into executable specifications, plans, and agent‑ready task breakdowns. Establish specification‑first review gates and living change proposals that align human engineers and AI agents before implementation begins.

Security, Governance & Session Management Implement enterprise‑grade security for agents including: OAuth and SSO flows IAM roles, service accounts, least‑privilege design Secure MCP tool access, command permissioning, and input validation Architect safe session‑based AI interactions with proper expiration, auditing, and context isolation. Ensure compliance with enterprise governance, Responsible AI requirements, and platform guardrails.

Platform Engineering, IaC & Dev Ops Use Terraform to build GCP infrastructure for AI workloads, vector stores, knowledge graphs, and orchestration services. Build CI/CD pipelines for model deployments and agent lifecycle automation. Implement observability, monitoring, and logging for AI service health.

Innovation & Collaboration Evaluate emerging tools and frameworks—including Claude Code, Git Hub Copilot, AWS Kiro, Git Hub Spec‑Kit, Open Spec, and BMAD‑METHOD—and integrate them into engineering workflows. Partner with architects, data engineers, and platform teams to implement cross‑domain AI capabilities. Document architecture…