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# AI Engineer

- **Company:** Greenlite Inc
- **Location:** New York, NY, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Computer-Aided Design, Geographic Information Systems, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Amazon S3, Continuous Integration, Fault Tolerance, Python (Programming Language), Software Product Management, TypeScript, Management of Software Versions, Pinecone, Retrieval-Augmented Generation, Llamaindex, AgentCore, Backend, Agentic-AI, CrewAI, AutoGen, Machine Learning Operations
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25197_53f49663bf051d1fe3b1095516a4055e

## About the Role

* Agent frameworks: Depth with LangGraph (graph orchestration, state, recovery) and Strands Agents (structured tool reasoning); experience integrating MCP clients/servers.
 * Production chops: 4-12+ years shipping high-reliability backend or ML systems (Python/TypeScript), cloud infra (AWS), containers (ECS/EKS), CI/CD, IaC, and secure integration patterns.
 * Retrieval & data: Hands-on with RAG, document stores/vector DBs (bonus: knowledge of Pinecone or early exposure to S3 Vectors), schema/versioning for code texts and comments.
 * Evaluation mindset: Ability to design realistic, auditable testbeds; familiarity with agent benchmarks (and their pitfalls) and how to turn real user flows into acceptance tests.
 * Safety & governance: Policy-driven guardrails (Grounding checks, topic/word filters), auditability, and human-in-the-loop controls.
 * Domain empathy: Curiosity for building codes, plan sets, and reviewer workflows; comfort working with messy PDFs/CAD and plan-review UX.
 * Bedrock AgentCore knowledge: Nice to have - comfort with Runtime (session isolation, 8-hour sessions, large payloads), Memory, Gateway, Identity, Observability, and built-in Browser/Code-Interpreter.
 * Nice-to-haves: CrewAI/AutoGen/LlamaIndex experience; Apryse or similar PDF tooling; geospatial data; prior work in AEC/reg-tech.

## Description

* Operationalize on Bedrock AgentCore: Use Runtime for secure, scalable hosting and streaming; Gateway to expose APIs as agent-ready tools; Memory for persistent context; Identity for least-privilege access; Observability for traces/metrics; and built-in Browser/Code-Interpreter where appropriate.
 * Evaluation & safety harness: Stand up task-level and end-to-end evals (success rate, cost, latency, human-handoff rate) and regression suites; borrow ideas from AgentBench/WebArena/SWE-bench where useful but bias toward domain-grounded tests for permitting.
 * Retrieval & knowledge: Partner with Data to wire agents to building-code knowledge (RAG), evaluate vector store options (incl. S3 Vectors preview, or Pinecone) and set up durable knowledge interfaces the agents can trust.
 * UX handoffs: Collaborate with Product/Design to craft agent-first reviewer UX in LiteTable (great inline citations, diffs, and remediation suggestions).
 * Quality & reliability: Build fault-tolerant flows (retries/rollbacks/compensation), observability dashboards, and on-call runbooks for agent incidents.
 * Technical leadership: Create internal libraries/templates for agent patterns; mentor engineers; review designs with domain experts (architects/code officials)., * 30 days: One production agent behind a feature flag with observability and a basic evaluation harness; measurable reduction in reviewer clicks/time on a scoped task in LiteTable. Hands on work with some aspect of our data to improve it for agentic work.
 * 60 days: A small portfolio of cooperating agents (intake + doc QA + code lookup) in shadow/beta with Guardrails and human-handoff paths; clear reliability SLOs.
 * 90 days: Participation in developing a plan for agents driving 25% faster reviews on target flows; stable on-call; cost/latency under thresholds; repeatable patterns/template repos useful for future agents.

Why Join Us?

 * Shape the data platform and labeling strategy that future hires will depend on.
 * Be an early technical leader on a high-impact AI product and trailblaze the first AI model for the building compliance industry.
 * Work directly with architects, engineers, and domain experts who shape the system
 * Contribute to making the built world more efficient, sustainable, and safe
 * Solve one of the most under-explored AI problems in the real world (with a huge market size)