AI Engineer- NLP

AI Engineer — NLP (Conversational Fleet Analytics)

Level: Mid to senior

About the role

V-Assistant is a conversational AI system running in production on Velocitor's VTrack fleet management platform. Users ask natural-language questions about vehicles, drivers, safety events, scorecards, and inspections, and get back formatted answers with charts and tables. Under the hood it is a LangGraph tool-calling agent over 28 domain tools that wrap the VTrack API, fronted by NeMo Guardrails, backed by PostgreSQL with pgvector for retrieval and agent checkpointing, and served to an embeddable React chat widget over an NDJSON stream. It is deployed across five environments on Azure Container Apps.

What you will work on

• Take over and then extend the core chat pipeline: guardrails, conversational query reformulation, embedding-based tool routing, the LangGraph agent, response formatting, and follow-up question generation.
• Maintain and add to the domain tool layer over the VTrack API, including argument schemas, authorization checks, pagination, date handling, and error formatting.
• Support the system in production: respond to incidents, investigate latency and quality regressions, and improve the telemetry and runbooks where the current instrumentation makes diagnosis harder than it should be.
• Improve retrieval quality for the RAG-backed knowledge tools using PostgreSQL full-text search and pgvector, and help decide where a hybrid approach is warranted.
• Contribute to an evaluation practice that gates model and prompt changes: representative and adversarial datasets, tool-selection and argument accuracy, shadow traffic, canary rollout, and automated rollback.
• Help reduce and control LLM cost and latency through per-request token and cost telemetry, prompt and context trimming, caching, model tiering, and elimination of redundant LLM stages.
• Strengthen security boundaries: tenant-scoped credentials and queries, server-side tool authorization independent of the model, and prompt-injection defense across the prompt, retrieval, tool, authorization, and output layers.
• Extend the tiered test strategy across commit, PR, nightly, and release gates

Technical environment

Backend: Python 3.12, FastAPI, Pydantic v2, SQLAlchemy 2 with Alembic, async psycopg/asyncpg, LangChain and LangGraph, Azure OpenAI via langchain-openai, NeMo Guardrails, ONNX Runtime embeddings via FastEmbed, LangFuse and structlog for observability, httpx, strict mypy and ruff, pytest with DeepEval.

Frontend: React 19, TypeScript, Vite, Tailwind v4, @assistant-ui/react for the chat runtime, TanStack Query, Radix UI, Recharts, MSW, Vitest and Testing Library.

Infrastructure: Azure Container Apps, Azure PostgreSQL Flexible Server with pgvector, Front Door, Key Vault, Container Registry, OpenTofu/Terraform across five environments, Azure DevOps Pipelines.

Architecture patterns: domain-driven design with domain, application, and infrastructure layers; CQRS in the L&D module; dependency injection container; UI/hook/connector separation on the frontend.