Senior Software Architect – Data Platform, AI/ML

Responsibilities

• 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

Requirements

• 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 such as Python, Java, C#, and 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 such as Databricks, Snowflake, BigQuery, and Redshift
• Expert-level SQL, query optimization, and performance tuning
• Expertise in development platforms and services including AWS, Visual Studio, Databricks, and GitHub
• 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