Sr Principal Data, Analytics & AI Engineer

Key Responsibilities: Own the technical design and architecture for agentic AI and data modernization solutions across the Global Supply Chain and MQ landscape, from problem framing through production support. Define standards and reusable patterns for cloud-based data modeling, pipeline design, agent orchestration, and evaluation that other engineers can build upon. Mentor junior and mid-level engineers and lead code and design reviews. Partner with senior business stakeholders and IT leadership to shape roadmaps, sequence delivery, and manage build versus buy trade-offs; direct and hold accountable external vendor and partner teams. Apply deep working knowledge of Global Supply Chain data domains, including material master, BOM and recipe, procurement, inventory and stock movements, production orders and batch execution, planning and scheduling, warehouse management, transportation management, distribution and logistics, and quality and deviations. Work across SAP (S/4HANA, ECC, BW/4HANA) and SHARP, translating source-system semantics into trusted, business-ready data models. Bring an end-to-end supply chain perspective, including S&OP, MRP, plan-to-produce, source-to-pay, order-to-cash, and capacity and inventory management, along with supporting analytics such as service level, inventory turns, cycle time, schedule adherence, and supply risk. Correlate supply chain and manufacturing data by linking ERP, planning, MES, historian, and quality sources across systems and time to create integrated business solutions. Collaborate with business and global teams on agentic solution design, requirement gathering, solution development, and delivery to drive business value. Design, build, and deploy AI agents and multi-agent workflows for manufacturing and supply chain decision support, including prompt engineering, tool/function calling, agent orchestration, and retrieval-augmented generation (RAG) over technical documents and operational data. Define agent performance measurement and improvement using test scenarios, evaluation criteria, accuracy and hallucination monitoring, user feedback loops, agent observability, and guardrails. Implement, test, and deploy machine learning and predictive models into production with attention to performance, scalability, and interpretability. Design, build, and maintain scalable, reliable data pipelines using modern ETL/ELT tooling to ingest, process, and transform large datasets for advanced analytics and agentic AI. Lead data modernization initiatives, migrating and re-platforming legacy supply chain reporting and data assets onto modern lakehouse and cloud architectures. Monitor and troubleshoot data quality and agent behavior issues to maintain data integrity and reliability across platforms. Develop and maintain documentation for data architecture, data flows, agent designs, prompts, model choices, and deployments to support maintainability, validation, and GxP/CSV requirements. Ensure compliance with relevant data privacy, security, and responsible AI requirements. Stay current with agentic AI and data engineering developments and apply practical, proven advances to team work.

Required Qualifications: Bachelor’s degree in Computer Science, Engineering, Data Science, Statistics, Supply Chain, or a related field with 8+ years in data engineering, analytics, or AI/ML, including hands-on production Gen-AI or agentic app delivery. Experience with supply chain and manufacturing data, hands-on SAP (S/4HANA/ECC, BW/4HANA), SHARP, and MES-related data and analytics. Hands-on advanced statistical and machine learning, including LLM and agentic app development (prompt engineering, tool/function calling, RAG, vector search/embeddings, and multi-step workflows). Deep knowledge of agent frameworks (LangChain, LangGraph, or equivalent) and production LLM APIs (OpenAI, Azure OpenAI, Anthropic, Bedrock), plus Azure/AWS/Databricks experience. Advanced Python (preferred), with SQL and experience with Java/Scala/R as additional strengths; PowerBi/Tableau and web app experience are also beneficial. Experience building end-to-end production-grade analytics systems. Technology StackSAP (S/4HANA, ECC, BW/4HANA), SAP IBP, SHARP, MES ETL, ELT, lakehouse, cloud architectures Python, SQL, Java, Scala, R, Power BI, Tableau LangChain, LangGraph, OpenAI, Azure OpenAI, Anthropic, Bedrock Azure, AWS, Databricks RAG, vector search/embeddings, prompt engineering, tool/function calling, agent orchestration, multi-step workflows

Benefits: Company bonus (depending in part on company and individual performance) 401(k) Pension Vacation benefits Medical, dental, vision, and prescription drug benefits Flexible benefits, such as healthcare and/or dependent day care flexible spending accounts Life insurance and death benefits Certain time off and leave of absence benefits Well-being benefits, including an employee assistance program, fitness benefits, and employee clubs and activities

Location: Indianapolis, Indiana Work schedule: Full time, 5 days per week onsite

Preferred Qualifications: Master’s degree in Computer Science, Engineering, Data Science, Statistics, Supply Chain, or a related field with 5+ years in data engineering, analytics, or AI/ML, including hands-on production Gen-AI or agentic app delivery. Experience in pharmaceutical, life sciences, or other regulated manufacturing supply chains. Track record of taking multiple AI or agentic solutions through to production, including evaluation, monitoring, and iteration after go-live. Strong command of manufacturing processes and metrics such as OEE, downtime, yield, batch performance, and right-first-time, alongside supply chain metrics. Experience with supply chain planning software (SAP IBP, Kinaxis, o9) and IoT or streaming operational data. Exposure to manufacturing data sources such as historians (OSI PI), MES/TrakSYS, PMX, or LIMS. Experience working in a GxP-regulated environment, including CSV and validation practices. Experience with data visualization tools (Power BI preferred). Applied knowledge of responsible AI practices, including model risk, bias, explainability, and data privacy. Experience leading globally distributed teams or vendor partners. Databricks, AWS, or Azure certifications. Compensation USD 133,500 - 224,400 per yearly. Actual compensation will depend on a candidate’s education, experience, skills, and geographic location.