Forward Deployment Engineer

Build and ship AI agents and automation harnesses as a core deliverable - not a side experiment - using tool use/function calling, multi-turn context management, and agentic design patterns (MCP, LangChain-style frameworks)

Use Claude, Cursor, and Codex as your primary development environment daily - build with AI, not around it, across every layer you touch

Evaluate and correct non-deterministic model output as a first-class engineering discipline - know what the AI wrote, where you overrode or discarded it, and what would have shipped broken if trusted blindly

Take a problem from rough idea to deployed, working software with minimal handoffs - writing code, shaping UX, and wiring data pipelines yourself, accelerated by AI tooling throughout

Design agent skills and internal AI-assisted workflows that other engineers on the team rely on and build from

Move across frontend, backend, data engineering, and infra within the same sprint, using AI tools to compress the time each layer normally takes

Design and maintain data pipelines and analytical surfaces on Databricks and AWS that non-engineers can actually use

Work directly with product managers and stakeholders - push back on scope, propose better (often AI-driven) solutions, and make pragmatic trade-offs without waiting to be told

Own architectural decisions for your product area, including when an agent/LLM-based approach is the right call versus deterministic code

Leave the codebase simpler than you found it - know when to abstract, inline, or simplify rather than add

Deploy, debug, and operate confidently in AWS without breaking production

Deliver outcomes that would take a conventional team 5-10x longer - the agentic/AI-native workflow itself is the reason for that multiplier, not just raw coding speed

Required Skills:

Frontend: React, TypeScript

Backend: Python or Node.js, REST/GraphQL APIs, event-driven service design

Data Engineering Databricks: (PySpark, Delta Lake, notebooks, workflows)

BI/Visualization: Streamlit, Tableau, Evidence (nice to have)