Sr AI Engineer

We are seeking a Senior AI Engineer to own production AI systems end to end - not just wire prototypes. This role sits at the intersection of agentic AI, full-stack engineering, and AI platform / DevOps. You will design and ship LLM applications, RAG pipelines, MCP tooling, and the infrastructure that makes them reliable in an enterprise environment. You will still work in our AI-assisted delivery flow (Lovable * GitHub * Claude Code * CI/CD * Vercel/GCP/Azure), but the bar is senior: you architect agent workflows, harden AI-generated code, own observability and deployment, and set patterns other engineers follow., Design and ship production agentic systems using LangChain, LangGraph, and related orchestration patterns (supervisor/worker, tool-calling, human-in-the-loop, recoverable state). Build and operate RAG pipelines: embeddings, hybrid retrieval, reranking, citation/grounding, and evaluation so answers stay accurate and auditable. Stand up and maintain MCP servers and tool integrations so agents can safely call enterprise systems (Snowflake, GitHub, Slack, internal APIs, knowledge bases). Work with vector databases and embedding workloads (pgvector / Supabase, Pinecone, Weaviate, FAISS, or equivalent) for semantic search and agent memory. Integrate multi-model LLM APIs (Claude, GPT, Gemini, Bedrock, etc.) with guardrails, cost/latency controls, and production observability.

Full-Stack Production Engineering

Take AI-generated React/Next.js (or equivalent TypeScript) front ends and turn them into secure, scalable full-stack applications. Wire UIs to backends, APIs, PostgreSQL/Supabase, Snowflake, Salesforce, and internal microservices. Implement Entra ID (Azure AD) SSO, OAuth 2.0, and RBAC. Design REST and GraphQL APIs with clear contracts, versioning, and enterprise auth.

AI-Assisted Development (Senior Bar)

Use Claude Code, Cursor, GitHub Copilot, and similar tools daily - and review, refactor, and harden AI-generated code before it ships (security, performance, maintainability). Define team standards for vibe-coded prototypes moving through GitHub, Harness, and production. Partner with Agent Developers and AI Solution Architects to land agent capabilities in real applications, not demos.

AI Platform, DevOps & Reliability

Own CI/CD (Harness, GitHub Actions, or equivalent) and deploy to Vercel, GCP (Cloud Run / GKE), Azure, and/or AWS. Implement observability with Dynatrace, Datadog, or equivalent (APM, logs, traces, alerting on both apps and LLM workflows). Use Docker/Kubernetes, IaC, and modern DevOps practices to keep AI services repeatable and recoverable. Apply supply-chain and app security: secrets management, image scanning, input validation, prompt-injection prevention, and secure coding of AI-generated output. Configure API gateways (Kong/Konnect or similar) for auth, rate limits, and traffic control.

7+ years professional software engineering, including significant production ownership (not just prototypes or coursework). 3+ years building and shipping LLM, RAG, or agentic systems in production - or equivalent depth owning an internal AI platform. Hands-on with LangChain and/or LangGraph (or comparable agent orchestration). Experience with vector databases / embeddings (pgvector, Pinecone, Weaviate, FAISS, OpenSearch, or similar). Experience building or integrating MCP servers / tool-calling interfaces for agents. Strong TypeScript and React / Next.js, plus backend in Python (FastAPI) and/or Node.js / Go. Production cloud experience on AWS, GCP, and/or Azure; containers and CI/CD in real environments. PostgreSQL and at least one cloud data platform (Snowflake, BigQuery, or equivalent). OAuth 2.0 / SSO (Microsoft Entra ID preferred). Proven ability to take AI-generated code and make it production-safe. Comfortable in Git workflows, code review, and hybrid collaboration. 3 days in office required. Daily use of Claude Code, Cursor, Lovable, or Copilot as a primary development workflow Supabase (Postgres + Auth + Edge Functions) and Vercel Snowflake MCP connectors or similar enterprise data-to-agent patterns Kubernetes, Terraform, GitOps Kong, Dynatrace, Datadog Evaluation harnesses, guardrails, prompt versioning, LLM observability (Langfuse, LangSmith, or similar) Retail, automotive, or dealership / field-ops technology