Job Description

IND Staff Software Engineer - GCC011

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.

Job Posting Title

Platform & Agentic AI Engineer

Justification

This requisition hires Senior AI Engineers who will:

Design and deliverproduction‑gradeAgentic AI systems using Google ADK, Anthropic MCP,LangGraphLangChain, 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.

The Senior AI Engineer will architect, build, and operationalize advanced AI and multi-agent solutionsleveragingRAG, GraphRAG, Agentic AI frameworks, andenterprise‑gradecloud 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 withAlloyDB’sAI/Agentic capabilities—including vector indexing, embedding support, and tight integration with Vertex AI—as well as strong fundamentals in PostgreSQL / Postgres RDSfor building retrieval systems, agent memory stores, and structured context-management layers.

The engineer mustdemonstratestrong foundational engineering skills in Python or Typescript,IaC(Terraform), DevOps pipelines, and secure distributed system design using GCP services such as Vertex AI, Cloud Run, Cloud Storage, andAlloyDB

Responsibilities

AI/Agentic System Architecture & Development

• Design and implement Agentic AI solutions using Google ADK,LangGraph,LangChain, and Agent Engine.

• Build advanced RAG andGraphRAGpipelines, vector retrieval systems, andknowledge‑graph–augmented reasoning.

Implement MCP-compliant agentswith 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‑stepworkflows and orchestration

• Session boundary and identity controls

• LeverageAlloyDBand PostgreSQL/RDSfor:

• Vector storage and hybrid search

• Agent memory persistence, session management, and state recovery

• Structured prompt scaffolding and fact retrieval

• ACIDcompliant transactional reasoninglayers‑complianttransactional reasoning layers

• Develop scalable AI microservices using Python/Typescript, Cloud Run, Vertex AI, and event-driven components.

• Optimizemodel inference, retrieval latency, and overall system performance.

Security, Governance & Session Management

• Implement enterprise-grade security for agents including:

• OAuth and SSO flows

• IAM roles, service accounts, leastprivilegedesign‑privilegedesign

• Secure MCP tool access, commandpermissioning, and input validation

• Architect safesession‑basedAI interactions with properexpiration, auditing, and context isolation.

• Ensure compliance with enterprise governance, Responsible AI requirements, and platform guardrails.

Platform Engineering,IaC& DevOps

• 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 like Claude Code, GitHub Copilot, AWS Kiro and integrate them into engineering workflows.

• Partner with architects, data engineers, and platform teams to implementcross‑domainAI capabilities.

• Document architecture patterns, reusable code modules, and standards for MCP/Agentic development.

Qualifications

Experience

• 6–8 years in software engineering, including 2+ years in GenAI, multi-agent, or LLM systems.

• Proven delivery of at least oneproduction‑gradeAI or Agentic system, preferably involving RAG orGraphRAG

Technical Expertise

Core Engineering

• Strong engineering fundamentals in Python and/or Typescript.

Agentic AI & Protocols

• Deep, practical experience with:

• MCP (Model Context Protocol)— tools, capabilities, memory, session orchestration, security

• Google ADK Agentic Protocols— agents, workflows, context management

Databases & Agent Memory Stores

• Hands‑onexperience withAlloyDB, including:

• Vector indexing /pgvector

• AI inference acceleration and Vertex AI integration

• Building agent memory and retrieval layers

• Transactional context management for Agentic systems

• Strong PostgreSQL/Postgres RDS fundamentals, including:

• Schema design for knowledge retrieval

• Query optimization

• Hybrid search patterns

• Durable storage for AI session and memory state

Cloud & Platform Skills

• Experience with:

• Vertex AI (Model Garden, Embeddings, Vector Search, Generative AI APIs)

• GCP Cloud Run,AlloyDB, Cloud Storage, Secret Manager

• Terraform /IaC

• CI/CD automation, containerization, environment provisioning

• OAuth, SSO, IAM roles/policies, service account management

Additional

• Experience with AI coding tools (Claude Code, GitHub Copilot, AWS Kiro).

• Strong understanding of LLM safety, governance, context window management, and prompt engineering.

Preferred Certifications

• GCP Professional Cloud Architect

• GCP Professional Machine Learning Engineer

Education

• Bachelor’s orMaster’s in Computer Science, Engineering, or related field.

About Us | Our Culture | What It’s Like to Work Here