Sr Software Architect - Data Platform & AI​/ML

Overview

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 are seeking a Senior Staff Software Architect who will split time between platform strategy and developing advanced AI solutions, architecting data pipelines, establishing secure development workflows, and delivering production applications used by teams daily.

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

• Architecture & Pipeline Development: Define end-to-end data platform architecture from data ingestion through GenAI development, 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 enabling seamless integration into dashboards, applications, and operational workflows.
• Develop backend APIs and services powering analytics applications; implement authentication, authorization, caching, and performance optimization.
• Create reusable UI components and application templates; establish design patterns and code standards for application development.
• Mentorship & Enablement: Mentor junior developers on software engineering best practices, patterns, and data modeling; conduct code reviews and provide feedback on quality, performance, security, and maintainability.
• Provide technical guidance on solution optimization and architecture; create training materials and documentation to enable the team to build applications independently.

• 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.