Sr AI Engineer - Platform Engineering
The Hartford17 days ago
Chicago, IL, USA +3 moreStaff+
Base Salary
$128k - $191k/yr
Responsibilities
- Design and implement production-grade agentic AI and multi-agent systems using Google ADK, LangGraph, LangChain, and Agent Engine.
- Build agent harnesses for execution loops, tool orchestration, context assembly, sub-agent delegation, streaming, token management, hooks, guardrails, and permissions.
- Develop LangChain/LangGraph orchestration, RAG and GraphRAG pipelines, vector retrieval, knowledge-graph reasoning, and MCP-compliant agents.
- Use AlloyDB and PostgreSQL/RDS for vector storage, hybrid search, agent memory, session state, retrieval, and transactional context management.
- Build scalable AI microservices and GCP infrastructure using Python or TypeScript, Cloud Run, Vertex AI, Cloud Storage, Terraform, and event-driven components.
- Drive spec-driven development with GitHub Spec-Kit, OpenSpec, and BMAD-METHOD, including executable specifications, review gates, and agent-ready task breakdowns.
- Implement OAuth, SSO, IAM, least-privilege access, secure MCP tooling, session isolation, auditing, governance, and Responsible AI controls.
- Build CI/CD, observability, monitoring, logging, and lifecycle automation for AI services and agent deployments.
- Evaluate AI coding and development tools and collaborate with architects, data engineers, and platform teams on reusable AI capabilities.
Requirements
- 6–8 years of software engineering experience, including 2+ years in GenAI, multi-agent, or LLM systems.
- Production delivery experience with at least one AI or agentic system, preferably involving RAG or GraphRAG.
- Strong Python and/or TypeScript engineering skills and experience designing secure distributed systems.
- Deep practical experience with MCP, Google ADK Agentic Protocols, LangChain, LangGraph, agent harnesses, and LangSmith.
- Hands-on experience with AlloyDB, vector indexing or pgvector, Vertex AI integration, agent memory, retrieval layers, and transactional context management.
- Strong PostgreSQL or Postgres RDS skills, including schema design, query optimization, hybrid search, and durable AI session and memory storage.
- Experience with Vertex AI, GCP Cloud Run, AlloyDB, Cloud Storage, Secret Manager, Terraform, CI/CD automation, containerization, OAuth, SSO, IAM, and service accounts.
- Experience with spec-driven development using GitHub Spec-Kit, OpenSpec, and BMAD-METHOD, including multi-agent planning and specification-first review gates.
- Understanding of LLM safety, governance, context-window management, prompt engineering, and secure agent design.
- Bachelor’s or master’s degree in Computer Science, Engineering, or a related field.
- GCP Professional Cloud Architect or GCP Professional Machine Learning Engineer certification is preferred.
- Experience with Claude Code, GitHub Copilot, or AWS Kiro is preferred.
Benefits
- Hybrid schedule requiring work in a Columbus, OH; Chicago, IL; Hartford, CT; or Charlotte, NC office three days per week, Tuesday through Thursday.
- Additional total compensation may include short-term or annual bonuses, long-term incentives, and on-the-spot recognition.
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