Job Description
Scope/What’s Driving the Need: Client has developed a prototype over the last few months for a new QMS and they are leveraging heavy AI and automation tools. One of our current consultants is leaving, but this isn’t technically a backfill for that profile, since they are in a different spot now current state than they were 4 months ago when our contractor began.
Contractor Skills & Experience
Advanced Education & Industry Experience
Master’s degree in a quantitative field with 7–10+ years of hands‑on experience applying AI/ML to medical device manufacturing, quality, and post‑market data with demonstrated, measurable impact.
- Agentic AI for Manufacturing Intelligence (Core Focus)
- Deep expertise designing and deploying agentic AI systems that autonomously reason across manufacturing, quality, and post‑market datasets, execute multi‑step analysis, self‑correct, and drive decisions with minimal human intervention.
- Production LLM Expertise (Claude‑Based)
- Proven, production‑grade experience using Claude LLMs (Claude 3.5+) for regulated use cases, including prompt orchestration, tool calling, structured output generation, guardrails, and audit‑ready logging.
- Unstructured → Structured Manufacturing Data Transformation
- Strong expertise converting unstructured medical device data (complaints, CAPAs, investigation reports, service notes, operator logs, SOPs, PDFs, emails) into structured, schema‑aligned datasets suitable for analytics, modeling, and regulatory review.
- AI‑Driven Quality & Failure Data Extraction
- Experience building AI pipelines for entity extraction, event classification, failure mode normalization, trend tagging, risk categorization, and summarization aligned to manufacturing and quality taxonomies.
- Core ML & Statistical Analysis for Manufacturing
- Strong foundation in predictive modeling, clustering, time‑series analysis, anomaly detection, and statistical inference applied to process parameters, yield, defects, equipment signals, and failure trends.
- Manufacturing Data Platforms & Engineering
- Advanced proficiency with Databricks (Spark, SQL, Delta Lake), Python, and SQL to ingest, structure, and analyze large‑scale manufacturing, quality, and post‑market datasets across millions of records.
- Quality, CAPA & Root Cause Analytics
- Demonstrated ability to correlate defects, NCRs, CAPAs, complaints, and service events with upstream manufacturing signals and process changes using data‑driven root cause methodologies.
- Enterprise & Regulated Systems (SAP‑Centric)
- Hands‑on experience extracting and analyzing data from SAP Tahiti, Salesforce, TrackWise and QMS data while maintaining data integrity, traceability, and compliance in regulated environments.
- Outcome‑Driven Delivery in FDA‑Regulated Contexts
- Track record of deploying AI systems that reduce investigation cycle time, improve defect detection, automate failure analysis, and deliver clear, defensible insights for manufacturing, quality, regulatory, and leadership teams.