Forward Deployment Engineer

TechDigital Group; Bloomfield (CT); On-site; USD 120,000 - 190,000; Full time

Job summary

TechDigital Group in Bloomfield, CT is seeking an experienced AI-focused engineer to build and ship AI agents and automation harnesses as core deliverables. You will work across frontend, backend, and data pipelines, using AI tooling daily and collaborating with product managers to define pragmatic, scalable solutions that move fast.

You will own architectural decisions for your product area, deploy and operate in AWS, and help the team deliver outcomes that supersede conventional timelines

Qualifications

• Experience building AI agents and tool-use patterns.
• Proficiency with React/TypeScript and Python/Node.js.
• Experience with Databricks and AWS.
• Experience in designing data pipelines and analytics surfaces.

Responsibilities

• Build and ship AI agents and automation harnesses as a core deliverable.
• Use Claude, Cursor, and Codex as your primary development environment daily.
• Evaluate and correct non-deterministic model output as a first-class engineering discipline.
• Take problems from rough idea to deployed software with minimal handoffs.
• Design agent skills and AI-assisted workflows for team-wide reuse.
• Move across frontend, backend, data engineering, and infra within sprints using AI tooling.
• Design and maintain data pipelines and analytical surfaces on Databricks and AWS.
• Collaborate with product managers and stakeholders to push back on scope and propose better AI-driven solutions.

Skills

AI tooling

React

TypeScript

Python

Node.js

REST/GraphQL

Databricks

AWS

Tools

Claude

Cursor

Codex

REST/GraphQL

Databricks

AWS

Job description

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)