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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), DevOps pipelines, and secure distributed system design using GCP services such as Vertex AI, Cloud Run, Cloud Storage, and AlloyDB.
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.
Qualifications
Experience
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.
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‑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
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 or Master’s in Computer Science, Engineering, or related field.
This role will have a Hybrid work schedule, with the expectation of working in an office (Columbus, OH, Chicago, IL, Hartford, CT or Charlotte, NC) 3 days a week (Tuesday through Thursday).
Candidates must be authorized to work in the US without company sponsorship. The company will not support the STEM OPT I-983 Training Plan endorsement for this position.
Compensation
The listed annualized base pay range is primarily based on analysis of similar positions in the external market. Actual base pay could vary and may be above or below the listed range based on factors including but not limited to performance, proficiency and demonstration of competencies required for the role. The base pay is just one component of The Hartford’s total compensation package for employees. Other rewards may include short-term or annual bonuses, long-term incentives, and on-the-spot recognition. The annualized base pay range for this role is:
$127,600 - $191,400Equal Opportunity Employer/Sex/Race/Color/Veterans/Disability/Sexual Orientation/Gender Identity or Expression/Religion/Age
About Us | Our Culture | What It’s Like to Work Here | Perks & Benefits
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What We Do
Human achievement is at the heart of what we do. We put our belief into action by not only ensuring individuals and businesses are well protected, but by going even further – making an impact in ways that go beyond an insurance policy
