S
Artificial Intelligence Engineer
SolomonEdwards
3 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.