Remote Forward Deployed Engineer: AI-Driven CX Solutions

Decision Lens

Arlington (VA)

Remote

USD 150,000 - 200,000

Full time

42 hours ago

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Job summary

Decision Lens is seeking a Forward Deployed Engineer to embed engineering, data, and application development into customer environments, delivering measurable mission outcomes. Operating at the intersection of product, engineering, and customer experience, you will own problems end-to-end—from discovery to deployment, and leverage AI-assisted tools to accelerate value while maintaining quality.

This remote-first role requires on-site work or travel to customer sites in Arlington, VA area and

Qualifications

Bachelor’s degree in computer science, engineering, information systems, or a related technical field, or equivalent practical experience.
5+ years in software engineering, forward‑deployed or field engineering, technical consulting, or enterprise application delivery.
Demonstrated ability to build working software end-to-end across frontend, backend, data, and workflow logic.
Experience working directly with customers in complex environments, translating ambiguous requirements into deployed software.
Strong understanding of APIs, data modeling, workflow logic, application security, and deployment patterns.
Hands‑on experience with AI‑assisted development tools, or a demonstrated willingness to adopt them responsibly.
Eligibility for Secret or above security clearance required; active clearance strongly preferred.
Travel requirements are based on customer needs and expected to be around 30%.

Responsibilities

Embed onsite with customers through discovery, trust-building, workflow mapping, solution design, deployment and stabilization.
Work cross functionally including with CX, engineering, sales, solutions engineering and functional SMEs to understand the customer’s challenges and design the best solution.
Translate ambiguity into a clear solution to be deployed within Decision Lens.
Proactively identify when the stated requirement is not the real problem, and ask the few critical questions early that prevent weeks of rework later.
Design data models that support decision workflows, prioritization, scoring, traceability, auditability, and executive reporting.
Take personal responsibility for the reliability, security, testing, and quality of everything deployed.
Use AI coding agents and AI-assisted tools such as Claude Code, Cursor, and GitHub Copilot to accelerate development, testing, debugging, and documentation.
Prompt, direct, and review AI-generated code while maintaining ownership of architecture, technical decisions, and quality—reviewing, testing, and hardening AI-assisted work before customer delivery.
Exercise strong judgment about where AI-generated code is useful and where it creates risk, and never expose sensitive customer data or controlled information in unauthorized tooling.
Run demo-driven development cycles with frequent customer feedback; show working software early and respond constructively when users challenge the application, data, or logic.
Stay engaged through stabilization until the application is usable, accepted, and has an identified operational owner—then exit cleanly so the solution never becomes permanently dependent on the individual FDE.
Produce crisp written plans, decision logs, implementation notes, and executive-ready updates, and communicate tradeoffs in business, operational, and mission terms.
Say “no,” “not yet,” or “phase two” gracefully when scope threatens the timeline, architecture, or mission outcome.
Identify reusable apps, templates, integration patterns, workflow components, and data models from field engagements, and contribute them back to Decision Lens repositories and playbooks.
Serve as the voice of the field to Product and Engineering, distinguishing one-off customer‑specific work from repeatable product opportunity.
Build with reuse and product leverage in mind.