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Artificial Intelligence Engineer
Linkedin
2 hours ago
Full-time
On-site
Charlotte, North Carolina, United States
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? Apply now to learn more about this opportunity.
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? Apply now to learn more about this opportunity.