Job Summary:
1. 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)
2. Use Claude, Cursor, and Codex as your primary development environment daily — build with AI, not around it, across every layer you touch
3. 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
4. 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
5. Design agent skills and internal AI-assisted workflows that other engineers on the team rely on and build from
6. Move across frontend, backend, data engineering, and infra within the same sprint, using AI tools to compress the time each layer normally takes
7. Design and maintain data pipelines and analytical surfaces on Databricks and AWS that non-engineers can actually use
8. 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
9. Own architectural decisions for your product area, including when an agent/LLM-based approach is the right call versus deterministic code
10. Leave the codebase simpler than you found it — know when to abstract, inline, or simplify rather than add
11. Deploy, debug, and operate confidently in AWS without breaking production
12. 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:
- AI Tooling: Claude, Cursor, Codex; LLM APIs (Anthropic, OpenAI); prompting, tool use, agent patterns, MCP
- Frontend: React, TypeScript
- Backend: Python or Node.js, REST/GraphQL APIs, event-driven service design
- Data Engineering Databricks: (PySpark, Delta Lake, notebooks, workflows)
- Cloud/Infra: AWS (S3, Lambda, Glue, Redshift), Infrastructure-as-Code (plus)
- BI/Visualization: Streamlit, Tableau, Evidence (nice to have)