Forward Deployment Engineer

Work directly with customer teams to scope valuable AI opportunities, build working prototypes in days, and turn them into adopted, measurable production systems.

New York, NY - On-site

Forward-Deployed AI Engineering

Full-time

Role Overview

As a Forward Deployment Engineer on Tactical Edge’s forward deployment team, you own the path from an ambiguous business problem to working AI software. You embed with customer leaders and operators, understand their workflows and data, define a practical business case, and build full-stack AI applications. You then partner with product, AI engineering, and security to evaluate, harden, and scale what works. Success means software people use, measurable operational improvements, and reusable patterns that make the next deployment better.

What You'll Do

Engage business leaders, operations owners, and technical teams to uncover high-value problems, challenge assumptions, and define where AI can help

Map existing workflows, data sources, and constraints; write lightweight scopes with clear inputs, outputs, success criteria, and credible estimates of time saved or cost reduced

Build working AI prototypes in days, put them in front of real users, and iterate based on field feedback

Develop full-stack applications across LLM integration, retrieval-augmented generation (RAG), tool-calling agents, REST APIs, data pipelines, and lightweight user interfaces

Own technical decisions from prompt design and orchestration through integrations and the user experience, and explain tradeoffs to engineering and business audiences

Use Claude and other approved AI development tools to accelerate implementation, testing, documentation, and solution design while reviewing and validating their output

Partner with product, AI engineering, and security to move prototypes into production with evaluations, access controls, audit logging, monitoring, and reliable failure handling

Demonstrate progress through live software, communicate impact and next steps clearly, and surface risks, dependencies, and blockers early

Drive customer adoption through hands-on enablement and track operational KPIs, hours saved, quality improvements, and failure modes against the agreed baseline

Document architecture, operating procedures, and handoffs; contribute reusable components, workflow patterns, and field learnings to the broader engineering team

What We're Looking For

Experience independently building and shipping end-to-end software, including APIs, data integrations, and user interfaces

Hands-on experience building applications with LLM APIs, including prompt design, tool use, RAG, and multi-step agent workflows; experience should go beyond using a chat interface

Proficiency using Claude or comparable AI coding tools for development, with the judgment to test generated code and validate system behavior

Evidence of rapid prototyping: examples of moving from a business conversation to working software in days, with a clear explanation of the problem and result

Ability to structure ambiguous operational problems into scoped solutions and simple, credible business cases with measurable success criteria

Clear communication with executives, operators, and engineers, including the ability to explain what you built, why it matters, its costs, and its limitations

Production-oriented judgment across security, data privacy, evaluation, monitoring, failure handling, and customer adoption

High ownership in customer environments and clear documentation that helps other teams maintain and extend your work

Preferred Experience

Forward-deployed, consulting, embedded delivery, or startup engineering experience with direct responsibility for customer outcomes

Hands-on use of the Claude API, Amazon Bedrock, AWS AI services, and Python or TypeScript tooling

Experience with LangGraph, LangChain, or equivalent frameworks for reliable tool-calling and multi-step agent orchestration

Delivery in regulated or operationally complex environments, working with security, governance, and compliance stakeholders on data handling, explainability, bias evaluation, and auditability

Experience turning customer-specific solutions into reusable components, accelerators, and deployment playbooks

How We Work

Outcome-driven

Solutions that run in production

Agentic by design

Systems that reason and act safely

Enterprise-first

Security, governance, scale

Small teams, high ownership

Direct impact on outcomes

What You'll Get

Hands-on ownership of customer-facing AI deployments
Exposure to real operational problems where AI needs to produce measurable outcomes
Collaboration across product, AI engineering, security, delivery, and customer teams
Competitive compensation (role/location dependent)
On-site collaboration in New York City

Hiring Process

Intro call

Context + fit

Field workflow discussion

Real customer deployment scenario

Cross-functional interview

AI / engineering / customer delivery

Final conversation

Alignment + offer

We value clear problem-solving and production readiness over theoretical perfection.

Apply for this role

View all roles