Sr Software Engineer

Job Description SummaryThe AI Systems Team is building the next generation of AI-assisted tooling for aerospace engine design. We’re looking for a growth-oriented AI Engineer to help transform GE Aerospace engineering, simulation, and manufacturing data into production-grade ML and data pipelines that shorten the design cycle for engines, turbines, and airframe components.This is a multi-faceted engineering role. You’ll spend most of your time writing and shipping code alongside AI engineers and data scientists, and you’ll also contribute to team planning and partner with design engineering and quality stakeholders to align on requirements, timelines, and success metrics. We’re looking for someone who’s excited to deepen their technical craft while communicating clearly across technical and non-technical audiences.What you’ll do: Design, build, deliver, and maintain software applications and services across ML, cloud, platform, and application domains. Own the full software lifecycle: requirements analysis, solution design, implementation, documentation/procedures, testing, deployment, and operational support. Convert complex engineering datasets into reliable, scalable workflows that enable modeling, inference, and decision support in design and manufacturing contexts.Job DescriptionRoles and Responsibilities:Define, develop, and evolve AI-enabled software products and platforms that accelerate aerospace design engineering workflows, leveraging large-scale simulation, test, and manufacturing dataProvide hands-on technical leadership for an Agile team of 8-10 engineers, setting architecture, coding standards, and delivery practices while remaining close to implementationPartner with Control Title Holders and engineering stakeholders to understand product vision and translate design-engineering needs (e.g., performance, durability, operability) into software and AI capabilitiesTranslate requirements into a prioritized backlog of epics/user stories, driving delivery to required timelines, quality, security, and operational standards.Collaborate with architects and domain experts to develop and execute multi-generation technology roadmaps for AI, data, and platform modernization (e.g., simulation-data pipelines, model serving, evaluation, and governance)Lead the design and implementation of data/ML pipelines that ingest and curate simulation outputs (CFD/FEA/thermal/structural), test data, and engineering metadata—enabling analytics, surrogate modeling, optimization, and AI-assisted decision supportBuild and operate cloud-native services on AWS and Azure, including secure storage, scalable compute, orchestration, and MLOps capabilities (e.g., automated training, reproducibility, and model lifecycle management)Drive increased efficiency across teams by eliminating duplication and enabling reuse through shared data products, common APIs, feature/model registries, templates, and reference architecturesEstablish and improve engineering processes across development, sustainment, and production support—improving reliability through observability, incident response playbooks, automated remediation, and post-incident learningsWork cross-functionally with other business departments (engineering, manufacturing, quality, IT/security) to align dependencies, compliance requirements, and deliverablesDrive world-class quality through rigorous SDLC practices: Lean/Agile/XP, CI/CD, automated testing, secure coding, scalability patterns, documentation-as-code, refactoring, and performance engineeringEnsure the team has clear understanding of business direction, strategy, priorities, and measurable outcomes; communicate consistently and transparentlyEngage subject matter experts to ensure successful transfer of complex domain knowledge (e.g., physics-based modeling assumptions, boundary conditions, mesh/solver settings) into scalable software abstractions and data standardsWrite production-quality code that meets standards and delivers intended functionality using the most appropriate technologies for the project (e.g., Python, Java, C#, TypeScript—based on system needs)Understand performance parameters for data-intensive and AI workloads; assess and improve application performance across compute, memory, storage, and networkApply strong fundamentals in data structures and algorithms, implementing efficient approaches for large datasets, scientific computing workflows, and high-throughput servicesProactively share information across the team and stakeholders with the right level of detail, strong timeliness, and clear technical rationaleRequired Qualifications:Bachelor’s Degree from an accredited college or university (or a high school diploma / GED with a minimum of 4 years of relevant working experienceAt least an additional 3 years of relevant working experienceDesired Characteristics:Proven experience building data platforms and ML systems for engineering/scientific data (simulation, test, telemetry, manufacturing, or similar).Strong cloud expertise across AWS and Azure, including architecture, security, and operationsExperience with MLOps practices: experiment tracking, reproducible training, model registry, CI/CD for ML, automated evaluation, monitoring/drift detection, and controlled rollouts.Experience building APIs and services for AI-powered applications (REST/gRPC), plus strong data access patterns and query optimization.Familiarity with modern engineering data formats and workflows (e.g., time-series, large unstructured results, metadata catalogs).Experience with Windows and Unix/Linux development environments.Understanding of simulation-driven design workflows and data (e.g., CFD, FEA, thermal, aero/structural analysis), including common pain points: traceability, configuration management, reproducibility, and data volume.Experience with surrogate modeling, optimization loops, or AI-assisted design exploration is a strong plus. Business AcumenDemonstrates initiative to explore alternate technolog