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Only W2- GenAI Forward Deployment Engineer (Python, React, AWS)
Digitive LLCAustin, TX🇺🇸United StatesPosted 22 Jul 2026
Why This Role Stands Out
This role offers a unique opportunity to be at the forefront of GenAI development, building impactful AI agents and automation solutions with cutting-edge tools. You'll thrive here if you're a mid-senior engineer with Python, React, and AWS expertise, eager to innovate across the full stack and contribute to a forward-thinking technology company. Apply now to shape the future of AI-driven engineering!
Quick Overview
Work Type
On Site
Level
Mid Senior
Job Description
Job Title: GenAI Forward Deployment Engineer (Python, React, AWS)
Location: **Hybrid at Bloomfield, CT or or Austin, TX**
Tenure: Until Dec 31st 2026
Interview Process: Internal & Client
**Remote profiles can be considered if profile is strong, candidate's who are able to work onsite will be prioritised**
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)
Interview Process: Internal & Client
**Remote profiles can be considered if profile is strong, candidate's who are able to work onsite will be prioritised**
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)
Skills
Node.js
AWS
Tableau
Databricks
GraphQL
LLM
Python
REST
React
Redshift
TypeScript
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