Senior Staff Software Architect – Data Platform, AI/ML

🕒 Ontem

🏈 Ohio – Remoto

🏈 Ohio – Remote

💵 $131.000 - $180.000 / ano

⏰ Tempo Integral

🟠 Sênior

🔙 Engenheiro Backend

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GE Aerospace

SiteLinkedInTodas as Vagas

10.000+ funcionários

🚀 Aeroespacial

🏭 Manufatura

🎖️ Defesa

💰 $2.000.000.000 Post-IPO Debt - GE Aerospace em 2025-07

Aerospace • Manufacturing • Defense

A GE Aerospace é uma empresa global de aeronáutica que projeta, fabrica e realiza manutenção em motores a jato, componentes e sistemas integrados para aeronaves comerciais e militares. Com sede em Cincinnati, Ohio, a empresa opera mais de 60 locais de fabricação, mais de 15 instalações de revisão e reparo de componentes, além de múltiplos centros de engenharia em 24 países, empregando cerca de 53. 000 pessoas. A GE Aerospace investe pesadamente em P&D (relatando US$ 2,75 bilhões, incluindo financiamentos de clientes/parceiros), apoia a aviação comercial em larga escala (3,4 bilhões de passageiros voaram com tecnologia GE em 2024) e mantém uma frota de cerca de 45. 000 motores em operação. A empresa enfatiza a fabricação nos EUA, o desenvolvimento da força de trabalho e um modelo operacional enxuto chamado FLIGHT DECK, sendo um importante fornecedor de defesa que apoia a aviação militar.

Descrição

• 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.

🎯 Requisitos

• 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.

🏖️ Benefícios

• 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