Senior Software Engineer – Platform & Agentic AI Engineering

The Hartford•Hartford, CT

1d•$127,600 - $191,400•Hybrid

About The Position

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. 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, and 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.

Requirements

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.
Strong engineering fundamentals in Python and/or Typescript.
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.
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.
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.
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.
Experience with AI coding tools (Claude Code, GitHub Copilot, AWS Kiro).
Strong understanding of LLM safety, governance, context window management, and prompt engineering.
Bachelor’s or Master’s in Computer Science, Engineering, or related field.

Nice To Haves

GCP Professional Cloud Architect
GCP Professional Machine Learning Engineer

Responsibilities

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.
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.
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.
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.
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.