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 **Domain & Business Acumen** + Experience building solutions for supply chain, manufacturing, maintenance, or operations is a strong plus + Understands business metrics and can translate platform capabilities into quantifiable business outcomes (cost savings, time reduction, forecast accuracy improvement) + Skilled in breaking down ambiguous problems, writing clear problem statements, and estimating model development effort accurately + Stays current on AI/ML and cloud platform industry trends (LLM advancements, new frameworks, emerging techniques); brings practical innovations backed by proof-of-concepts **Leadership & Collaboration** + Leads by example through delivering AI/ML products and platform engineering while mentoring team on AI integration, prompt engineering, and model usage + Able to work through ambiguity and drive alignment between AI capabilities and business needs; communicates model limitations, confidence intervals, and uncertainty clearly to non-technical stakeholders + Continuously measures solutions against user expectations while balancing competing priorities and maintaining build quality **Personal Attributes** + Strong written and verbal communication skills with the ability to explain complex AI/ML concepts simply and translate effectively between data scientists, software engineers, and business stakeholders + Effective collaborator who works seamlessly with BI developers, AI engineers, and business stakeholders + Business-minded approach that focuses on operational metrics, user needs, and business impact while designing AI and platform solutions that solve real problems rather than technical exercises + Persists to completion by driving products through deployment, monitoring, and iteration while taking ownership of model performance and continuously improving accuracy The base pay range for this position is $131,000-180, 000. Proven experience with cloud data warehouses/lakehouses (Databricks, Snowflake, BigQuery, Redshift) + Expert-level SQL, query optimization, and performance tuning + Expertise in development platforms and services: AWS, Visual Studio, Databricks, GitHub, etc.