Build reusable geospatial AI components including embeddings, similarity search, retrieval, feature engineering, and agentic pipeline modules. Process imagery, map, sensor, and traffic data; support foundation-model training, evaluation, and fine-tuning; and deliver tested, documented components to MLOps for production serving. The role requires strong Python and software engineering fundamentals, deep learning experience, retrieval-system knowledge, and familiarity with high-volume spatial data.

Lead the architecture, development, deployment, and monitoring of production-grade machine learning systems for high-definition, ADAS, ISA, and IVI maps. Lead work across computer vision, NLP, deep learning, Generative AI, Agentic AI, MLOps, data ingestion, model training, inference, and scalable cloud or in-vehicle deployment. Provide technical leadership, conduct architecture and code reviews, integrate ML outputs with mapping workflows, and translate business requirements into reliable ML system designs.

Leads AI/ML strategy, technical vision, and delivery of scalable machine learning platforms for map editing and maintenance. Drives generative AI, agentic AI, computer vision, LLM, automation, MLOps, governance, observability, security, and responsible AI adoption. Mentors data scientists, leads multidisciplinary initiatives, translates business needs into measurable solutions, and influences executives, customers, vendors, and partners.