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