Full Stack AI + Data Engineer

Role Overview

We're looking for a Full Stack AI + Data Engineer who owns the complete product lifecycle — from discovery and ideation through to production deployment. The role sits at the center of client delivery and internal accelerator development — someone who can own the full arc of a project and be the person who makes things real.

What You Will Do

Own the full product lifecycle — from discovery and ideation through system design, build, and production deployment. Design and build RAG pipelines, agentic workflows, and multi-agent systems using LangGraph and the broader AI agent ecosystem.

Why It Might Be a Fit

You've shipped full-stack AI applications in production — not just demos or PoCs. You're comfortable switching between designing a data model in the morning and debugging an agentic pipeline in the afternoon.

Requirements

• Production-grade experience across AI engineering, full-stack development, and data — with genuine depth, not just surface familiarity.
• AI & GenAI Engineering
• RAG pipeline design — retrieval strategies, vector stores, chunking, re-ranking, and evaluation
• Agentic AI systems using LangGraph — multi-agent coordination, tool use, memory, and state management
• Building and composing AI capabilities via MCP servers, Skills, and Plugins
• AI-paired accelerated programming — proficient at using Claude Code or a comparable agentic coding tool as a daily productivity layer
• Full Stack Development
• Python — primary language, used for services, automation, and data work
• FastAPI — async REST API design, dependency injection, testing
• Next.js — component architecture, server-side rendering, state management, and UX sensibility
• PostgreSQL — schema design, query optimization, indexing
• System Design — can architect a system from a blank page: services, boundaries, trade-offs, and scale
• Automation & Workflow Orchestration
• Building automation workflows using Celery or Temporal — task queuing, retries, distributed scheduling
• Event-driven patterns and async processing at the application layer
• Data Engineering
• ETL/ELT pipeline design — batch, incremental, and event-driven ingestion patterns
• Data pipelining and modeling — dimensional modeling, EDW design, schema governance
• dbt — transformation logic, testing, documentation, and analytics engineering best practices
• DevOps & Cloud
• CI/CD and deployment — GitHub Actions or equivalent, containerized delivery, environment management
• Exposure to common cloud services on AWS and Azure — compute, storage, managed databases, serverless

Benefits

• Fully remote position — work from anywhere, globally
• High-autonomy, high-ownership work — you drive the technical direction
• Exposure to cutting-edge AI engineering across multiple industries and use cases
• Opportunity to shape the Practice's internal accelerators and reusable assets
• A team of sharp engineers who take craft seriously and skip the bureaucracy