Forward Deployment Application Engineer

Intuitive Machines•Houston, TX

About The Position

Build and deploy AI applications that improve spacecraft engineering, manufacturing, and services. As a member of the AI Strike Team, the engineer works directly with engineers and business users to turn prioritized problems into useful software through six-week delivery sprints. The role owns application delivery from discovery and prototyping through integration, evaluation, deployment, and technical handoff. Success is measured by improvements in cycle time, engineering effort, quality, or program execution, supported by reliable applications used in real workflows. The initial focus includes self-service access to approved data for LLM use; connectors that help users access data; use-case-specific subsystem assistants; curated datasets requiring extraction, transformation, and loading; and flight software development tools operating within an approved GovCloud AI environment. Proven workflows may evolve into bounded agents when their reliability and value justify it. The Product Lead sets priorities and business outcomes, with AI Council oversight. The engineer provides feasibility estimates and owns application implementation. The Core Platform and DevSecOps team supplies approved infrastructure and shared services, while domain engineers validate technical outputs and retain approval authority. The engineer also provides demonstrations, technical guidance, and usage instrumentation to support training and adoption.

Requirements

Must be a US citizen or permanent resident.
Bachelor’s degree; four additional years of experience may be substituted for a degree.
Minimum of 15 years of software, application, solutions, or data engineering experience, with evidence of shipping and supporting software used by real customers.
Strong Python and API-development skills, plus experience building usable application interfaces in TypeScript, JavaScript, or a comparable framework.
Hands-on experience delivering LLM applications, including retrieval, structured outputs, tool integration, and evaluation; ability to distinguish model limitations from data, workflow, and software defects.
Experience integrating APIs, databases, documents, and enterprise systems; practical understanding of authentication, authorization, data modeling, and data quality.
Proficiency with Git, automated testing, CI/CD, containers, and cloud deployment; ability to debug across application, integration, and infrastructure boundaries.
Ability to lead technical discovery, explain tradeoffs clearly, work through incomplete requirements, and deliver a bounded solution within a short sprint.
Sound judgment in handling sensitive information, maintaining traceability, and designing workflows with accountable human review.

Nice To Haves

Experience in aerospace, defense, manufacturing, or another regulated engineering environment; familiarity with configuration control, quality processes, and engineering verification.
Experience with AWS GovCloud and Bedrock, enterprise model gateways, Open WebUI, MCP interfaces, and secure development practices for CUI and ITAR workloads.
Integrations with PLM, ERP, MES, or ALM systems such as Teamcenter, IFS, Solumina, Jama, or GitLab; exposure to Nastran or other engineering analysis tools.

Responsibilities

Embed with users to observe work, map handoffs and bottlenecks, establish baselines, and define a feasible MVP with the Product Lead and domain owner.
Build application interfaces, backend services, APIs, and workflow automation; present prototypes to users early and improve them through observed use.
Integrate enterprise and engineering systems through reusable connectors, APIs, and data transformations while preserving source permissions, revisions, configuration, units, and provenance.
Implement retrieval-augmented generation, structured outputs, and tool calling with grounded answers, citations, and explicit human approval where needed.
Create representative evaluation cases and regression tests; measure task completion, retrieval accuracy, review effort, latency, reliability, and operating cost.
Deploy through approved GitLab CI/CD and DevSecOps patterns; implement monitoring, error handling, access controls, recovery, and sustainment documentation.
Provide feasibility estimates and own application implementation.
Provide demonstrations, technical guidance, and usage instrumentation to support training and adoption.
Within 30 days, understand a priority workflow, establish its baseline, confirm data and environment readiness, and demonstrate a useful prototype.
By the end of the first six-week sprint, deliver an evaluated pilot to intended users, document results and limitations, and support a scale, iterate, or stop decision.
Within 90 days, move a successful pilot toward supported production, demonstrate measured improvement including human review effort, and contribute reusable components for subsequent sprints.