Artificial Intelligence Engineer

AI Engineer Our client, is looking for an AI Engineer to own and evolve a production conversational AI system. This assistant lets users ask natural-language questions and get back formatted answers, charts, and tables — powered by a LangGraph tool-calling agent, guardrails, and retrieval-augmented generation at scale. What You'll Do • Extend the core chat pipeline: guardrails, query reformulation, embedding-based tool routing, agent orchestration, and response formatting • Maintain and grow a large domain tool layer over an internal API (schemas, auth checks, pagination, error handling) • Support a live production system: triage incidents, investigate latency/quality regressions, improve telemetry and runbooks • Improve RAG retrieval quality using PostgreSQL full-text search and pgvector, including hybrid approaches • Build evaluation practices that gate model/prompt changes: adversarial datasets, tool-selection accuracy, shadow traffic, canary rollouts, auto-rollback • Reduce LLM cost/latency via token telemetry, prompt trimming, caching, and model tiering • Strengthen security: tenant-scoped data access, server-side authorization, and prompt-injection defenses • Expand automated testing across commit, PR, nightly, and release gates Must-Haves • 3+ years building production backend services, including at least one shipped LLM-backed feature • Proven ability to take ownership of unfamiliar codebases safely using tests and traces • Strong async Python with strict typing discipline (mypy) • Hands-on experience with LangChain, LangGraph, or similar agent frameworks (tool/function calling) • Solid PostgreSQL skills: schema design, performance tuning, migrations, connection pooling • Experience operating live services: diagnosing issues from telemetry, rollback judgment • Understanding of AI security boundaries — treating model/retrieved content as untrusted, enforcing authZ outside the model • Comfort debugging across service boundaries using traces and correlation IDs • Familiarity with retries, backoff, circuit breakers for rate-limited APIs • Testing beyond unit tests — contract tests, eval of non-deterministic systems Nice to Have • Experience with vendor-to-in-house system handovers • Azure (Container Apps, OpenAI deployments, Key Vault) • Terraform/OpenTofu, CI/CD pipelines • Vector search/RAG at scale, chunking, reranking • LLM-as-judge evaluation awareness • Guardrails frameworks (e.g., NeMo Guardrails) • Modern React/TypeScript exposure • Data privacy/retention engineering experience Why Apply? Join a high-impact engineering effort shaping how real users interact with complex operational data through AI — with strong ownership, modern tooling, and real production stakes. 📩 Ready to make an impact?