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.

Overview The Senior AI Engineer will architect, build, and operationalize advanced AI and multi-agent solutions leveraging RAG, GraphRAG, Agentic AI frameworks, and enterprise‑grade cloud engineering.

A key requirement is robust, practical experience implementing MCP and ADK Agentic Protocols, with a solid understanding of:

Agent memory

Session and context lifecycle management

Tooling interfaces

Secure capability boundaries

Permissions and role enforcement

Additionally, candidates must have hands-on experience with AlloyDB’s AI/Agentic capabilities—including vector indexing, embedding support, and tight integration with Vertex AI—as well as strong fundamentals in PostgreSQL / Postgres RDS for building retrieval systems, agent memory stores, and structured context-management layers.

The engineer must demonstrate strong foundational engineering skills in Python or Typescript, IaC (Terraform), Dev Ops pipelines, and secure distributed system design using GCP services such as Vertex AI, Cloud Run, Cloud Storage, and AlloyDB.The role additionally requires deep, hands-on experience building and extending agent harnesses—the runtime scaffolding that orchestrates the agent execution loop, tool invocation, dynamic context-window assembly, sub-agent delegation, and guardrail and permission enforcement—together with production expertise in Lang Chain and Lang Graph.

Fluency in spec-driven, agentic development frameworks such as Git Hub Spec-Kit, Open Spec, and BMAD-METHOD, used to translate intent into executable specifications and orchestrate AI-assisted delivery at enterprise scale.

Responsibilities 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 retrievalACID‑compliant transactional reasoning layers

Develop scalable AI microservices using Python/Typescript, Cloud Run,…