Engr 3, Software

Engr 3, Software#26-13581 $60-$70 per hour San Diego, CA All On-site Job Description Pre Qualifying questiosn must beanswered and attached to the resume 1) What is your approach to converting fragmented, messy, unstructured quality data into an intelligence layer that leadership can get insights from? 2) How would you decide which use cases should be handled by deterministic analytics, traditional ML, RAG, agentic AI, or human review? Give examples. 3) What is the most complex task you have performed in Databricks? Bill rate •-•Remote Hours PSor est hours only Education Master's Data engr or Computer science 7+ Years in AI using Databricks and Medical Device

REQUIRED

Job Description Responsibilities:

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. Agentic AI for Manufacturing Intelligence Deep expertise 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. Production LLM Expertise (Claude‐Based) Production‐grade experience using Claude LLMs within orchestrated agent workflows, including prompt management, tool calling, structured outputs, guardrails, and audit‐ready logging. Unstructured → Structured Manufacturing Data Transformation 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. AI‐Driven Quality & Failure Data Extraction 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. Core ML & Statistical Analysis for Manufacturing Solid foundation in predictive modeling, clustering, time‐series analysis, anomaly detection, and statistical methods applied to manufacturing processes, defects, equipment signals, and failure trends. Manufacturing Data Platforms & Engineering 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. Quality, CAPA & Root Cause Analytics Demonstrated ability to correlate complaints, NCRs, CAPAs, and service data with upstream manufacturing signals using data‐driven root cause and investigation approaches. Enterprise & Regulated Systems (SAP‐Centric) 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.

Shift:

['AI Engineering', 'Anthropic Claude

AI', 'MCP

Server Customization', 'Microsoft Azure Databricks', 'SalesForce', 'SAP Tahiti', 'Trackwise']

Start:

['Microsoft Power Business Intelligence (BI)', 'Speech to Text tools', 'Text to Speech Tools']

EEO:

"Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of - Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans."