Forward Deployment Engineer - W2 Only

Saransh Inc; Bloomfield (CT); On-site; USD 120,000 - 180,000; Full time

Job summary

Saransh Inc in Bloomfield, CT is seeking a Forward Deployment Engineer to build and ship AI agents and automation harnesses as core deliverables using tool use, function calling, and multi-turn context management. You will deploy and operate in AWS, design cross-cutting AI-enabled workflows, and own architectural decisions for your product area while collaborating with product managers and stakeholders.

Daily work spans front-end, back-end, data engineering, and infra, with Claude, Cursor, and

Qualifications

• Experience building AI-powered agents and automation workflows.
• Ability to design scalable data pipelines and infra-centric solutions.
• Proficiency with AWS services and modern deployment practices.

Responsibilities

• Build and ship AI agents and automation harnesses as a core deliverable.
• Own architectural decisions for your product area; decide when AI/agent approach is right.
• Collaborate across frontend, backend, data, and infra within sprints using AI tooling.

Skills

AI Tooling

Frontend

Backend

Data Eng

Cloud Infra

BI/Visualization

Tools

Claude

Cursor

Codex

LLM APIs

React

TypeScript

Python

Node.js

Job description

• 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

Job title: Forward Deployment Engineer

Location: Bloomfield, CT or Austin, TX

W2 Position

Name

Claude

Large Language Model (LLM)

AWS

Python

React

Type Script

Job Summary

• 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

• 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)