Senior Software Architect - Data Platform & AI​/ML

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#, Type Script-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/lake houses (Databricks, Snowflake, Big Query, Redshift)
• Expert-level SQL, query optimization, and performance tuning
• Expertise in development platforms and services: AWS, Visual Studio, Databricks, Git Hub, etc.
• Experience implementing security…