Senior Software Architect - Data Platform & AI/ML

at GE Aerospace in Boise, Idaho, United States

Job Description

Job Description Summary

The Commercial Engine Services Business Intelligence team is building AI-enabled analytics solutions that empower faster problem solving and decision making across commercial and operational domains. We’re seeking a Senior Software Architect who will split their time between building our platform strategy and developing advanced AI solutions. You’ll architect data pipelines that feed the AI applications you build, establish secure development workflows, and ship production applications that teams use daily.

This is a dual-focus role combining software architecture and application development. You’ll establish data transformation pipelines that convert raw operational data into analytics-ready datasets, then build the AI applications, forecasting tools, and operational solutions that consume that data. You’ll implement security and CI/CD workflows, mentor junior developers on software engineering practices, and participate in AI/ML model development.

Job Description

Roles and Responsibilities:

Architecture & Pipeline Development

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

Security, Development Workflows & Platform Enablement

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

AI/ML Product Development

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

Mentorship & Enablement

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

Required Qualifications

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

Desired Characteristics

Technical Expertise

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

Domain & Business Acumen

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

Leadership & Collaboration

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

Continuously measures solutions against user expectations while balancing competing priorities and maintaining build quality

Personal Attributes

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

The base pay range for this position is $131,000-180, 000. The specific pay offered may be influenced by a variety of factors, including the candidate’s experience, education, and skill set. This position is also eligible for an annual discretionary bonus based on a percentage of your base salary/ commission based on the plan. This posting is expected to close on May 28th, 2026.

GE Aerospace offers comprehensive benefits and programs to support your health and, alon

Salary : $131,000 - $180,000