Senior Staff Software Architect – Data Platform, AI/ML

🕒 il y a 9 jours

🏈 Ohio – Distant

🏈 Ohio – Remote

💵 $131 000 - $180 000 / an

⏰ Temps Plein

🟠 Senior

🚰 Ingénieur Data

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GE Aerospace

Site WebLinkedInTous les Emplois

10 000+ employés

🚀 Aérospatiale

🏭 Fabrication

🎖️ Défense

💰 €2 000 000 000 Post-IPO Debt - GE Aerospace en 2025-07

Aerospace • Manufacturing • Defense

GE Aerospace est une entreprise aérospatiale mondiale qui conçoit, fabrique et entretient des moteurs à réaction, des composants, et des systèmes intégrés pour les avions commerciaux et militaires. Basée à Cincinnati, Ohio, l'entreprise exploite plus de 60 sites de fabrication, plus de 15 sites de révision et de réparation de composants, ainsi que plusieurs centres d'ingénierie à travers 24 pays, employant environ 53 000 personnes. GE Aerospace investit massivement dans la R&D (2,75 milliards de dollars rapportés, incluant les financements de clients/partenaires) et soutient l'aviation commerciale à grande échelle (3,4 milliards de passagers ont volé avec la technologie GE en 2024). Elle maintient une flotte d'environ 45 000 moteurs en service. L'entreprise met l'accent sur la fabrication aux États-Unis, le développement de la main-d'œuvre, et un modèle opérationnel allégé appelé FLIGHT DECK, et est un important fournisseur de défense soutenant l'aviation militaire.

Description

• Define end-to-end data platform architecture from data ingestion through GenAI development by translating business requirements into technical solution designs and implementation roadmaps • Implement scalable architecture for AI solutions spanning machine learning, natural language processing, multimodal AI, and agentic systems • Architect multi-layer data transformation pipelines and design data models optimized for analytics and AI/ML workloads including dimensional schemas, feature stores, and aggregate tables • Build production-grade transformation code that converts raw operational data into trusted, analytics-ready datasets; implement incremental loading, schema evolution, and backward compatibility • Establish data quality and observability frameworks including automated validation, schema drift detection, lineage tracking, and data cataloging to support discoverability and trust • Ensure data architecture aligns with enterprise standards, cybersecurity requirements, data governance policies, and compliance obligations • Design and implement data security architecture; define access controls, data classifications, and retention policies that meet company compliance policies • Establish development workflows—branching strategies, pull request standards, code review processes, and deployment procedures • Build CI/CD pipelines for analytics applications and data transformations; implement automated testing, security scanning, and deployment automation • Build monitoring and alerting for both data pipelines and applications—tracking failures, performance, costs, and user issues • Define, build, and evolve AI-powered software products that accelerate operations including LLM applications, machine learning models, and intelligent automation for supply chain optimization • Develop Model Context Protocol (MCP) servers that package domain-specific AI capabilities for reuse across the enterprise • Package AI/ML models as robust, well-documented APIs that enable seamless integration into dashboards, applications, and operational workflows • Develop backend APIs and services that power analytics applications; implement authentication, authorization, caching, and performance optimization • Create reusable UI components and application templates that accelerate solution development; establish design patterns and code standards for application development • Mentor junior developers on software engineering best practices, application development patterns, and data modeling • Conduct code reviews for team contributions; provide feedback on code quality, performance, security, and maintainability • Provide technical guidance on solution optimization and application architecture • Create training materials and documentation that enable the team to build applications independently.

🎯 Exigences

• Bachelor's Degree in Computer Science, Software Engineering, Data Science, or related field from an accredited university • A minimum of 3+ years of hands-on experience in software architecture, including building data platforms, pipelines, or applications in production environments AND 2+ years building or integrating AI/ML models, applications, or intelligent features • Write 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) • Experience building and implementing cloud data platforms; understanding of data architecture, ETL/ELT patterns, and data management best practices. Proven experience with cloud data warehouses/lakehouses (Databricks, Snowflake, BigQuery, Redshift) • Expert-level SQL, query optimization, and performance tuning • Expertise in development platforms and services: AWS, Visual Studio, Databricks, GitHub, etc. • Experience implementing security frameworks, access controls, and deployment automation • Familiarity with ML workflows, feature engineering, and model deployment; able to integrate AI/ML into applications • Experience with prompt design, LLM orchestration, and agentic workflows / multi-agent systems • Experience building solutions for supply chain, manufacturing, maintenance, or operations is a strong plus • Understands business metrics and can translate platform capabilities into quantifiable business outcomes (cost savings, time reduction, forecast accuracy improvement) • Skilled in breaking down ambiguous problems, writing clear problem statements, and estimating model development effort accurately • Stays current on AI/ML and cloud platform industry trends (LLM advancements, new frameworks, emerging techniques); brings practical innovations backed by proof-of-concepts • Leads by example through delivering AI/ML products and platform engineering while mentoring team on AI integration, prompt engineering, and model usage • Able to work through ambiguity and drive alignment between AI capabilities and business needs; communicates model limitations, confidence intervals, and uncertainty clearly to non-technical stakeholders • Strong written and verbal communication skills with the ability to explain complex AI/ML concepts simply and translate effectively between data scientists, software engineers, and business stakeholders • Effective collaborator who works seamlessly with BI developers, AI engineers, and business stakeholders • Business-minded approach that focuses on operational metrics, user needs, and business impact while designing AI and platform solutions that solve real problems rather than technical exercises • Persists to completion by driving products through deployment, monitoring, and iteration while taking ownership of model performance and continuously improving accuracy.

🏖️ Avantages

• 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 • 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