Senior Software Engineer – Platform & Agentic AI Engineering

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 and conversation state
• Tool authorization
• Multi-step workflows and orchestration
• Session boundary and identity controls

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.
• 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

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.
• 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.

Work Schedule

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).

Equal Opportunity Employer

The company is an equal opportunity employer and does 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