Senior AI Engineer (Full-Stack)

Role Description

We are looking for a Senior AI Engineer with strong full-stack capabilities to join our engineering team. You will work across multiple client-facing projects that sit at the intersection of AI, data, and web—starting with a live production AI agent platform. You should be comfortable owning features end-to-end, from LLM pipeline design through to a polished frontend experience.

What You'll Do

AI Agent Engineering

Design and build AI agent pipelines, including multi-node LangGraph graphs.

Implement intent routing, multi-turn conversational context, session state management, and tool integrations.

Develop multi-step reasoning pipelines and graph-based agent workflows.

RAG & Knowledge Systems

Build and maintain Retrieval-Augmented Generation (RAG) systems.

Design vector search architectures, embedding pipelines, retrieval grounding, and chunking strategies.

Implement hallucination mitigation techniques and retrieval evaluation frameworks.

LLM Integration & Optimization

Integrate and optimize Large Language Models (LLMs) including OpenAI, Gemini, and Anthropic.

Develop structured output workflows using JSON schemas.

Create effective prompt engineering strategies, few-shot examples, and context window management solutions.

Build provider-neutral client architectures to support multiple LLM vendors.

Full-Stack Development

Design, develop, and deploy end-to-end product features.

Build scalable FastAPI backends and React/Next.js frontends.

Implement Server-Sent Events (SSE) streaming and REST API contracts.

Deliver production-ready UI features independently without requiring dedicated frontend support.

Observability & Quality

Own LLM observability including:

Token usage logging

Cost tracking

Fallback detection

Performance monitoring

Regression test suites

Build evaluation pipelines and golden test suites to ensure AI quality and consistency.

Client & Product Collaboration

Collaborate directly with clients and stakeholders to understand business requirements.

Translate requirements into scalable, maintainable software solutions.

Keep technical documentation, specifications, and test coverage aligned with product changes.

Qualifications

Must-Have Skills

AI Agent Engineering

LangGraph or equivalent graph-based agent frameworks.

Multi-step reasoning pipelines.

Tool usage and orchestration.

State management and conversational workflows.

RAG & Vector Search

End-to-end RAG pipeline design and implementation.

Experience with vector databases such as:

Pinecone

Qdrant

pgvector

Weaviate

Chunking strategies and retrieval optimization.

Retrieval evaluation methodologies.

LLM Integration

OpenAI, Gemini, and Anthropic SDKs.

Prompt engineering and prompt optimization.

Structured JSON outputs.

Context window management.

Multi-provider LLM integrations.

Python Backend Development

Python 3.12

FastAPI

Async Python

Pydantic

SQLite

PostgreSQL

Redis

Pytest

Full-Stack Development

React

Next.js

TypeScript

Modern frontend architecture

API integration and state management

ML Engineering Fundamentals

Evaluation pipelines

Golden datasets and test suites

Regression tracking

Model performance monitoring

Good to Have

GIS & Mapping

ArcGIS REST APIs

GeoJSON

MapLibre GL JS

Spatial queries

(Strong advantage for initial project assignments.)

Data Visualization

Recharts

D3.js

Equivalent charting libraries

Cloud & DevOps

Docker

Azure

AWS

CI/CD pipelines

OIDC Authentication

Product Thinking

Ability to understand and interpret Figma designs.

Evaluate trade-offs between engineering effort and business value.

Deliver solutions aligned with business objectives.

Technologies You'll Work With

Agent Frameworks: LangGraph, LangChain

LLM Providers: Gemini, OpenAI, Anthropic

Backend: Python 3.12, FastAPI, SQLite, Redis

Frontend: Next.js 15, React 19, TypeScript, Zustand

Data & Visualization: Recharts, GeoJSON, MapLibre GL JS

Infrastructure: Docker, Azure Pipelines, Azure AD

What We're Looking For

Someone who can independently own a feature from requirements gathering to production deployment.

Strong full-stack engineering capabilities with no hand-holding required between backend and frontend development.

Strong engineering judgment and the ability to push back when shortcuts introduce hallucination risks, reliability issues, or technical debt.

Comfortable working in ambiguous environments with evolving client requirements.

Experience delivering software in real-world production environments.

Excellent communication skills with the ability to explain AI system behavior and limitations to non-technical stakeholders.

Strong documentation and testing discipline.

Nice to Have (Domain Experience)

Experience in any of the following industries is a significant advantage, though not required:

Energy

Oil & Gas

Infrastructure

Enterprise GIS