Senior Software Architect – Data Platform, AI/ML

Stellenbeschreibung:

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

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