Sr Staff Software Engineer at Thehartford

You're a seasoned Software Engineer with 6-8 years of experience, including at least 2 years focused on GenAI, multi-agent, or LLM systems, and you're comfortab

Work type: hybrid

Location: Columbus OH-Worth Ave | Charlotte NC-South Tryon St. | Morristown,NJ-67 Park Place E | Hartford CT- Home Office | Chicago, IL-200 W Madison St

Salary: $127,600 – $191,400/yr

Type: Full-time

Summary

You're a seasoned Software Engineer with 6-8 years of experience, including at least 2 years focused on GenAI, multi-agent, or LLM systems, and you're comfortable with a Bachelor's or Master's degree in a related field. **What makes it worth a look...** This is a full-time, hybrid role at The hartford with a substantial annual salary range of $127,600 - $191,400, offering flexibility across multiple office locations. **You might be a good fit if you...** * Have practical experience implementing MCP and ADK Agentic Protocols. * Are proficient with AlloyDB, including vector indexing and integration with Vertex AI, alongside strong PostgreSQL skills. * Can demonstrate foundational engineering skills in Python or Typescript, IaC (Terraform), and GCP services.

Job Description

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.

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.

ResponsibilitiesAI/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.

QualificationsExperience

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 ExpertiseCore 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 - $191,400

Equal Opportunity Employer/Sex/Race/Color/Veterans/Disability/Sexual Orientation/Gender Identity or Expression/Religion/Age

[About Us](https://www.thehartford.com/about-us) | [Our Culture](https://www.thehartford.com/about-us/corporate-culture) | [What It’s Like to Work Here](https://www.thehartford.com/careers/our-employees) | [Perks & Benefits](https://www.thehartford.com/careers/benefits)

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