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Tier-1 employer, metro location, mid-level ML/GenAI role with broad skills yields high competition.
Core GenAI and ML production skills are transferable, but reliability domain expertise increases fit sensitivity.
Explicit 3+ years, GenAI/ML, GCP and LLM framework requirements make filters highly stringent.
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Design, develop, and productionize AI-driven solutions for warranty forecasting, reliability risk analysis, and decision support within Ford's analytics ecosystem.
Lead development of Agentic AI applications and traditional ML models ensuring solutions are safe, observable, robust, and scalable on cloud platforms (GCP/AWS/Azure).
Collaborate with data scientists, engineers, and business stakeholders to translate complex problems into practical AI systems that improve business impact and efficiency.
Bachelor’s or Master’s degree in a quantitative field such as Computer Science, AI, Statistics, Mathematics, or Engineering; PhD preferred.
Minimum 3+ years professional experience in Data Science or Software Engineering with focus on Generative AI/LLM and traditional Machine Learning.
2+ years hands-on experience with supervised and unsupervised learning and statistical modeling.
Expert proficiency in Python including frameworks like LangGraph, LangChain, and Google ADK; experience with cloud platforms (GCP/AWS/Azure) and services such as Vertex AI, Cloud Run, and BigQuery.
Technical expertise at the intersection of applied generative AI and traditional statistical ML, capable of building production-ready, governed AI systems.
Experience architecting complex AI agent workflows that integrate diverse data sources and maintain strong safety and observability standards.
Skilled in translating complex technical challenges into practical AI solutions with effective collaboration across technical and non-technical teams.