Senior AI Engineer

The Hartford•Columbus, OH

1d•Hybrid

About The Position

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), DevOps pipelines, and secure distributed system design using GCP services such as Vertex AI, Cloud Run, Cloud Storage, and AlloyDB.

Requirements

6–8 years in software engineering, including 2+ years in GenAI, multi-agent, or LLM systems.
Proven delivery of at least one production‑grade AI or Agentic system, preferably involving RAG or GraphRAG.
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‑on experience with AlloyDB, 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

Nice To Haves

Experience with AI coding tools (Claude Code, GitHub Copilot, AWS Kiro).
Strong understanding of LLM safety, governance, context window management, and prompt engineering.
GCP Professional Cloud Architect
GCP Professional Machine Learning Engineer
Bachelor’s or Master’s in Computer Science, Engineering, or related field.

Responsibilities

AI/Agentic System Architecture & Development Design and implement Agentic AI solutions using Google ADK, LangGraph, LangChain, and Agent Engine.
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‑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.
Security, Governance & Session Management Implement enterprise-grade security for agents including: OAuth and SSO flows IAM roles, service accounts, least privilege design‑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 & 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 implement cross‑domain AI capabilities.
Document architecture patterns, reusable code modules, and standards for MCP/Agentic development.