Sr Principal Data, Analytics & AI Engineer

Lead the architecture and production delivery of near-real-time data foundations and agentic AI multi-agent workflows supporting Global Supply Chain and MQ.

Responsibilities

• Own 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 on.
• Mentor junior and mid-level engineers; lead code and design reviews.
• Partner with senior business stakeholders and IT leadership to shape roadmaps, sequence delivery, and manage build vs. buy decisions.
• 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 to translate source-system semantics into trusted, business-ready data models.
• Provide an end-to-end supply chain process viewpoint (S&OP, MRP, plan-to-produce, source-to-pay, order-to-cash, capacity and inventory management) and 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 data across systems and time to deliver integrated solutions enabled by data modernization and agentic AI.
• Collaborate with business and global teams on agentic solution design, requirement gathering, development, and delivery to drive business value.
• Design, build, and deploy AI agents and multi-agent workflows for automated analysis and decision support across manufacturing and supply chain use cases, including prompt engineering, tool/function calling, agent orchestration, and RAG over technical documents and operational data.
• Define how agent performance is measured and improved, including test scenarios, evaluation criteria, accuracy and hallucination monitoring, user feedback loops, agent observability, and guardrails for safe, grounded, in-scope behavior.
• Implement, test, and deploy machine learning and predictive models into production with focus on performance, scalability, and interpretability.
• Design, build, and maintain scalable, reliable data pipelines using modern ETL/ELT tooling to support advanced analytics, reporting, and agentic AI.
• Lead data modernization efforts to migrate and re-platform 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 advancements and apply practical, proven improvements to team delivery.

Requirements

• Bachelor’s in Computer Science, Engineering, Data Science, Statistics, Supply Chain, or related field, plus 8+ years in data engineering, analytics, or AI/ML with hands-on production Gen-AI/agentic app delivery.
• Experience with supply chain/manufacturing data; hands-on experience with SAP (S/4HANA/ECC, BW/4HANA) and SHARP; experience with MES and related data/analytics.
• Hands-on advanced statistical and ML/LLM development, including prompt engineering, tool/function calling, RAG, vector search/embeddings, and multi-step workflows.
• Deep knowledge of agent frameworks such as LangChain, LangGraph, or equivalent, and production LLM APIs (OpenAI, Azure OpenAI, Anthropic, Bedrock) plus Azure/AWS/Databricks exposure.
• Advanced Python (preferred); SQL required; Java/Scala/R plus; PowerBI/Tableau and web apps experience.
• Experience developing end-to-end production-grade analytics systems.

Technologies

• SAP (S/4HANA, ECC, BW/4HANA), SAP IBP, SHARP, MES
• ETL, ELT, lakehouse, cloud architectures
• Python, SQL, Java, Scala, R, PowerBi, 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) and pension
• Vacation benefits
• Medical, dental, vision, and prescription drug benefits
• Flexible benefits (e.g., 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 (e.g., employee assistance program, fitness benefits, employee clubs and activities)

Additional Information

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

Preferred Qualifications

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

Compensation

• $133,500 - $224,400 per year
• Actual compensation depends on education, experience, skills, and geographic location.