Define end-to-end data platform architecture from data ingestion through GenAI development
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
Build production-grade transformation code that converts raw operational data into trusted, analytics-ready datasets
Establish data quality and observability frameworks including automated validation, schema drift detection, lineage tracking, and data cataloging
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
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
Define, build, and evolve AI-powered software products
Develop Model Context Protocol (MCP) servers that package domain-specific AI capabilities for reuse across the enterprise
Create reusable UI components and application templates

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
2+ years building or integrating AI/ML models, applications, or intelligent features
Write production-quality code that meets standards and delivers intended functionality using appropriate technologies (e.g., Python, Java, C#, TypeScript)
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

Healthcare benefits include medical, dental, vision, and prescription drug coverage
Access to a Health Coach from GE Aerospace
Employee Assistance Program providing 24/7 confidential assessment, counseling and referral services
Retirement benefits include the 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

Senior Software Architect – Data Platform, AI/ML

Key skills