Principal Build & Release Engineer

GE Aerospace•Grand Rapids, MI

2d•$152,000 - $202,000•Onsite

About The Position

The AI Engineer is the senior individual contributor who owns the end-to-end build and productionization of AI enabled engineering capabilities across the business organizations of an aero engine and propulsion OEM. The role takes the reference architecture and patterns set by the AI Architect and turns them into robust, secure, scalable production services with great UX, multimodal, agentic LLM and other AI solutions, hardened against real engine engineering data and workflows across design, simulation, test, manufacturing, and sustainment. It embeds with the engineering teams of its assigned business, owns the build and release lifecycle for the capabilities it delivers, and stays hands on, architecting and building services and making pragmatic engineering tradeoffs rather than reviewing from a distance. Deep credibility in the physics and engineering of the product, turbomachinery, aerodynamics, combustion, heat transfer, structures, and materials, and product mindset shaped by certified, safety critical hardware are core to the role.

Requirements

Bachelor’s degree in Computer Science or a STEM major
Minimum of 10 years of overall engineering experience
Production AI/ML systems and end to end service delivery experience required throughout.
US person required; ability to obtain a US security clearance.
Active clearance preferred

Nice To Haves

Working fluency in the physics and engineering of aero engines and turbomachinery, aerodynamics, combustion, heat transfer, structures and rotordynamics, and materials, with the judgment to tell where a model is physically credible; hands on familiarity with CFD, FEA, and thermal/structural simulation, engine and rig test data, surrogate and physics informed modeling, model based systems engineering, the digital thread, and PLM.
Strong software development in Python and at least one systems language (C++, Rust, Go, or Java), with solid API, version control, and testing practice.
Strong DevSecOps and MLOps capability: cloud (AWS, Azure, or GCP), Docker and Kubernetes, CI/CD, infrastructure as code, observability, and tooling such as MLflow or Kubeflow, with security applied across DevOps and MLOps.
Applied production experience with LLM, retrieval augmented generation, and agentic frameworks, including multimodal AI, building robust, secure, scalable services with strong UX, and moving solutions from prototype to scaled production.
Ability to architect and build end to end services and integrate them with the digital thread, PLM, and existing enterprise and simulation systems.
Working knowledge of configuration choices and related cost implications, compute, model hosting, and build vs. buy, and experience with complex solution configurations.
Experience delivering inside aerospace & defense or comparable regulated constraints: ITAR / export control, CMMC, RMF / ATO, and airgapped or impact level environments.
Interprets internal and external business challenges and recommends best practices to improve products, processes, and services; stays informed of industry trends to inform decision making.
Understands how the internal and external business model works, engine and propulsion programs, certification, and service / MRO, and uses that understanding to prioritize delivery.
A product mindset for safety critical, certified hardware: comfort with verification and validation, certification evidence, reliability, and the long product and service lifecycle of an engine program.
Able to articulate the value of what is most important to the business, applying presentation and storytelling concepts for cross functional teams.
Acts as an expert resource and technical thought leader for colleagues with less experience, providing design guidance and mentoring.
Strong oral and written communication, with strong interpersonal and leadership skills and the ability to communicate and influence effectively across all organizational levels.
Demonstrated ability to lead programs and projects, with established project management skills and the ability to document, plan, market, and execute programs.
Demonstrated ability to work with and/or lead blended teams, including third party partners and customer personnel.
Demonstrated change management / acceleration capabilities.
Proven analytical and problem resolution skills, with the ability to influence and build consensus across engineering, IT, and platform teams and leadership.
Ability to negotiate internally and with external partners, vendors, and customers.

Responsibilities

Develop and own product build and release procedures and the end-to-end delivery of AI solutions, for a single release or multiple releases, working across the immediate team and other teams across the businesses.
Productionize multimodal, agentic LLM and other AI solutions, applied to engine and propulsion engineering problems across design, analysis, test, manufacturing, and sustainment, as robust, secure, scalable systems with great UX; architect and build end to end services.
Ground every capability in the realities of aero engine, propulsion, and turbomachinery engineering, integrating AI with CFD, FEA, thermal and structural analysis, engine and test data, model-based systems engineering, the digital thread, and PLM, so it augments how engines are designed, analyzed, tested, certified, and sustained.
Lead others to find creative solutions within complex, interdependent engineering and production processes with technical variety, turbomachinery design, analysis, and test cycles, employing sophisticated operational / product management, manufacturing, and engineering techniques, drawing on multiple internal and external resources and questioning conflicting data to arrive at decisions.
Take the reference architecture, patterns, and governance set by the AI Architect and deliver against them, keeping solutions compliant with data, security, and export control requirements (ITAR / export control, CMMC, RMF / ATO) across classified and unclassified environments.
Own the model and agent delivery lifecycle in production: data pipelines, CI/CD, containerized deployment, model and agent serving, monitoring, and the build and release pipeline.
Interpret internal and external business challenges and recommend best practices to improve products, processes, and services; use industry trends to inform the decision making process.
Lead the delivery of projects with moderate to significant technical complexity and risk; present business and technical discipline solutions to senior leaders.
Inform technical investment decisions, compute, model hosting, tooling, and build vs. buy, and the resourcing needed to deliver, working within program and project budgets.
Communicate complex messages and negotiate internally and with external partners, vendors, and customers to adopt a point of view and influence peers to act.
Act as an expert resource and best practice authority for the engineers embedded across the businesses, providing technical leadership, design review, and mentorship without direct management authority.
Develop peer, cross functional, and cross business relationships to maximize best practice sharing and team effectiveness.
Transition delivered capabilities to program ownership with documentation and trained users rather than leaving orphaned pilots.
Share technical, procedural, and business knowledge with others, and support a continuous learning culture through learning content, presentations, and mentoring.

Benefits

Healthcare benefits include medical, dental, vision, and prescription drug coverage
access to a Health Coach from GE Aerospace
Employee Assistance Program, which provides 24/7 confidential assessment, counseling and referral services
GE Aerospace Retirement Savings Plan, a 401(k) savings plan with company matching contributions and company retirement contributions
access to Fidelity resources and planning consultants
tuition assistance
adoption assistance
paid parental leave
disability insurance
life insurance
paid time-off for vacation or illness