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 #J-18808-Ljbffr