Senior AI Engineer, Platform Engineering

The Hartford

Confirmed live today High trust

Hybrid

Quick summary

Work type: Hybrid
Location: Hartford, CTCharlotte, NCColumbus, OHChicago, IL
Salary: $127,600–$191,400 / yr
Employment: Full-time
Posted: 87 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $182k

This role $160k

$107k $238k

below market most similar roles pay here above market

This role pays less than 60% of similar roles. Most pay $151,000–$214,000 — the blue band above. At the midpoint, this role pays about $160k versus about $182k for comparable roles.

Based on 240 similar postings.

Employer

About The Hartford

The Hartford is a leading provider of property and casualty insurance, group benefits, and mutual funds, serving businesses and individuals across the United States. Industry: Insurance & Financial Services

The Hartford currently has 70 open roles on FindRole.

Listed pay typically runs $127,600–$191,400 across 29 roles with salary data.

Most-posted roles

View all roles at The Hartford

At a glance

TL;DR · Senior AI Engineer, Platform Engineering

Role Posting Log in to save

JOB TITLE: Sr AI Engineer - Platform Engineering The Sr AI Engineer - Platform Engineering joins the platform engineering team to design and deliver production-grade Agentic AI systems. This role involves architecting and operationalizing advanced multi-agent solutions, building agent harnesses to orchestrate execution loops, and developing scalable AI microservices. The engineer will build reusable frameworks, APIs, and platform capabilities using Python, Typescript, Terraform, and GCP services like Vertex AI, Cloud Run, and Cloud Storage. Key responsibilities include implementing RAG and GraphRAG pipelines, managing agent memory via AlloyDB and PostgreSQL, and utilizing LangChain and LangGraph for stateful orchestration. The position requires expertise in MCP and ADK Agentic Protocols, as well as spec-driven development frameworks like GitHub Spec-Kit and OpenSpec to translate product intent into executable specifications within an enterprise environment.

What does a AI Engineer earn?

Median $211200 from 139 postings across 39 companies.

See salary data

Skills

Python Typescript LangChain LangGraph Terraform GCP Vertex AI AlloyDB PostgreSQL RAG GraphRAG Agentic AI CI/CD OAuth IAM LangSmith GitHub Spec-Kit OpenSpec BMAD-METHOD

What you'll do

Design and deliver production-grade Agentic AI systems using Google ADK, LangGraph, and LangChain.
Build agent harnesses to manage execution loops, tool orchestration, sub-agent delegation, and guardrail enforcement.
Develop advanced RAG and GraphRAG pipelines using AlloyDB and PostgreSQL for vector storage and memory persistence.
Implement MCP-compliant agents with secure tool invocation, session state management, and identity controls.
Drive spec-driven development by translating product intent into executable specifications using GitHub Spec-Kit and OpenSpec.
Build scalable AI microservices using Python or Typescript on GCP services like Vertex AI and Cloud Run.
Provision GCP infrastructure and CI/CD pipelines for AI workloads using Terraform.
Implement enterprise-grade security including OAuth flows, IAM roles, and secure MCP tool access.

What we're looking for

Bachelor’s or Master’s in Computer Science, Engineering, or a related field.
6–8 years of software engineering experience, 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.
Strong engineering fundamentals in Python and/or Typescript.
Deep practical experience with MCP, Google ADK Agentic Protocols, LangChain, and LangGraph.
Hands-on experience with AlloyDB (vector indexing, Vertex AI integration) and PostgreSQL/Postgres RDS.
Proficiency with Terraform, GCP services (Vertex AI, Cloud Run, Cloud Storage), and CI/CD automation.
Experience with spec-driven development frameworks like GitHub Spec-Kit, OpenSpec, and BMAD-METHOD.
GCP Professional Cloud Architect or GCP Professional Machine Learning Engineer certifications (preferred).

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