AI Data Engineer
About The Position
Responsible to support the BDash AI-powered data analytics platform. This individual will contribute to advance data engineering pipelines, AI agent development, and cross-functional quality analytics across different areas of the business such as quality, product engineering, reliability, field service and business strategy. The role involves deep expertise in designing and deploying agentic AI systems using agentic frameworks and orchestrators to reason across manufacturing, quality, and post market data, execute multi-step analysis, self-correct, and drive decisions with limited human intervention. It also requires production-grade experience using Claude LLMs within orchestrated agent workflows, including prompt management, tool calling, structured outputs, guardrails, and audit-ready logging. Additionally, the role focuses on building AI-driven data pipelines to transform unstructured medical device data into structured, analytics and review-ready datasets, developing orchestrated AI pipelines for entity extraction, event classification, failure mode standardization, trend tagging, risk categorization, and summarization aligned to quality and manufacturing taxonomies. A solid foundation in core ML and statistical analysis for manufacturing is essential, as is advanced proficiency with Databricks, Spark, SQL, Delta Lake, and Python. The role also requires demonstrated ability to correlate complaints, NCRs, CAPAs, and service data with upstream manufacturing signals using data-driven root cause and investigation approaches, and hands-on experience integrating and analyzing data from SAP Tahiti, Salesforce, TrackWise, and QMS platforms while maintaining traceability, data integrity, and compliance in regulated environments.
Requirements
- AI Engineering
- Anthropic Claude AI
- MCP Server Customization
- Microsoft Azure Databricks
- SalesForce
- SAP Tahiti
- Trackwise
- Deep expertise designing and deploying agentic AI systems using agentic frameworks and orchestrators.
- Production grade experience using Claude LLMs within orchestrated agent workflows, including prompt management, tool calling, structured outputs, guardrails, and audit ready logging.
- Strong expertise building AI driven data pipelines that transform unstructured medical device data (complaints, CAPAs, investigations, service notes, SOPs, PDFs, emails) into structured, analytics and review ready datasets.
- Experience developing orchestrated AI pipelines for entity extraction, event classification, failure mode standardization, trend tagging, risk categorization, and summarization aligned to quality and manufacturing taxonomies.
- Solid foundation in predictive modeling, clustering, time series analysis, anomaly detection, and statistical methods applied to manufacturing processes, defects, equipment signals, and failure trends.
- Advanced proficiency with Databricks, Spark, SQL, Delta Lake, and Python to ingest, structure, and analyze large scale manufacturing, quality, and post market data, supporting downstream analytics and AI systems.
- Demonstrated ability to correlate complaints, NCRs, CAPAs, and service data with upstream manufacturing signals using data driven root cause and investigation approaches.
- Hands on experience integrating and analyzing data from SAP Tahiti, Salesforce, TrackWise, and QMS platforms while maintaining traceability, data integrity, and compliance in regulated environments.
Nice To Haves
- Microsoft Power Business Intelligence (BI)
- Speech to Text tools
- Text to Speech Tools
Responsibilities
- Support the BDash AI-powered data analytics platform.
- Advance data engineering pipelines.
- Develop AI agents.
- Perform cross-functional quality analytics across quality, product engineering, reliability, field service, and business strategy.
- Design and deploy agentic AI systems using agentic frameworks and orchestrators.
- Reason across manufacturing, quality, and post-market data.
- Execute multi-step analysis.
- Self-correct AI systems.
- Drive decisions with limited human intervention.
- Utilize Claude LLMs within orchestrated agent workflows.
- Manage prompts, tool calling, structured outputs, guardrails, and audit-ready logging.
- Build AI-driven data pipelines to transform unstructured medical device data into structured, analytics and review-ready datasets.
- Develop orchestrated AI pipelines for entity extraction, event classification, failure mode standardization, trend tagging, risk categorization, and summarization.
- Apply core ML and statistical analysis to manufacturing processes, defects, equipment signals, and failure trends.
- Ingest, structure, and analyze large-scale manufacturing, quality, and post-market data using Databricks, Spark, SQL, Delta Lake, and Python.
- Correlate complaints, NCRs, CAPAs, and service data with upstream manufacturing signals.
- Integrate and analyze data from SAP Tahiti, Salesforce, TrackWise, and QMS platforms.
- Maintain traceability, data integrity, and compliance in regulated environments.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
