Employment Type: Full-Time
Location: Remote
About The Role
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.
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
LayerTechnology StackAgent FrameworksLangGraph, LangChainLLM ProvidersGemini, OpenAI, AnthropicBackendPython 3.12, FastAPI, SQLite, RedisFrontendNext.js 15, React 19, TypeScript, ZustandData & VisualizationRecharts, GeoJSON, MapLibre GL JSInfrastructureDocker, 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
