Sr AI Engineer - Platform Engineering
Overview
Application Skills
Application Category Skills
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.
This requisition hires Senior AI Engineers who will:
Design and deliver production‑grade Agentic AI systems using Google ADK, Anthropic MCP, LangGraph/LangChain, 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.
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.
The role additionally requires deep, hands-on experience building and extending agent harnesses—the runtime scaffolding that orchestrates the agent execution loop, tool invocation, dynamic context-window assembly, sub-agent delegation, and guardrail and permission enforcement—together with production expertise in LangChain and LangGraph.
Fluency in spec-driven, agentic development frameworks such as GitHub Spec-Kit, OpenSpec, and BMAD-METHOD, used to translate intent into executable specifications and orchestrate AI-assisted delivery at enterprise scale.
Responsibilities
AI/Agentic System Architecture & Development
Design and implement Agentic AI solutions using Google ADK, LangGraph, LangChain, and Agent Engine.
Build and extend agent harnesses, implementing the agent execution loop, tool-call orchestration, dynamic prompt and context assembly, sub-agent delegation, streaming, token-budget management, and hook and guardrail enforcement.
Engineer advanced LangChain and LangGraph orchestration, including LCEL chains, stateful graphs, checkpointing, human-in-the-loop workflows, memory, retrievers, callbacks, and LangSmith tracing and evaluation.
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
Develop scalable AI microservices using Python/Typescript, Cloud Run, Vertex AI, and event-driven components.
Optimize model inference, retrieval latency, and overall system performance.
Spec-Driven & Agentic Development
Drive spec-driven development (SDD) using frameworks such as GitHub Spec-Kit, OpenSpec, and BMAD-METHOD, translating product intent into executable specifications, plans, and agent-ready task breakdowns.
Establish specification-first review gates and living change proposals that align human engineers and AI agents before implementation begins.
Security, Governance & Session Management
Implement enterprise-grade security for agents including:
OAuth and SSO flows
IAM roles, service accounts, least‑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 and frameworks—including Claude Code, GitHub Copilot, AWS Kiro, GitHub Spec-Kit, OpenSpec, and BMAD-METHOD—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
LangChain & LangGraph — LCEL chains, agents, tools, memory, retrievers, stateful graph orchestration, checkpointing, human-in-the-loop control, and LangSmith tracing and evaluation
Agent harness engineering — agent execution loops, tool-call orchestration, context and prompt assembly, sub-agent delegation, streaming, token-budget management, and hook and guardrail enforcement
Spec-Driven & Agentic Development Frameworks
Hands-on experience with spec-driven development (SDD) workflows and tooling, including GitHub Spec-Kit (specify, plan, tasks, implement), OpenSpec (change proposals and living specifications), and BMAD-METHOD (agentic planning with specialized agent roles)
Proven ability to decompose product intent into executable specifications, structured plans, and agent-ready task breakdowns that align human and AI contributors before code is written
Familiarity with greenfield and brownfield delivery driven by multi-agent planning, context engineering, and specification-first review gates
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