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Lead development and refactor optimization algorithms in Python for pricing and supply‑chain problems. Design and implement predictive models and mathematical optimization solutions, collaborate with cross‑functional teams, coach data scientists, and translate business requirements into data‑driven pricing and replenishment strategies.

Lead analytics consulting engagements: translate business problems into end-to-end data science and ML solutions, present to executive stakeholders, manage projects and global teams, and operationalize analytic outputs with IT and data engineering.

Embed with client engineering teams to deploy, integrate, and scale enterprise generative AI solutions. Build agentic systems, train and serve LLMs with Vertex AI, integrate models with BigQuery/AlloyDB/Spanner, implement streaming inference pipelines, and ensure operational reliability while collaborating with stakeholders to translate business goals into production-grade architectures.

The GenAI Engineer will design, build, and deploy Generative AI solutions, focusing on reinforcement learning and multi-agent workflows, while collaborating with various teams to enhance model performance.

Design and lead the development of cloud-native Generative AI platforms and solutions, ensuring scalability, security, and integration with enterprise applications.

Design, build, and deploy scalable ML solutions using GCP and Vertex AI. Collaborate with teams to operationalize models and monitor performance.

Define enterprise AI strategy and architecture for LLMs, RAG, and predictive models; design secure, high-performance REST/GraphQL APIs and event-driven systems; establish AI/data governance; and lead cross-functional teams to productionize real-time AI applications.

As a Senior Agentic AI Engineer, you will lead innovation in AI initiatives, from ideation to prototyping, collaborating with teams to deliver impactful solutions.

As a Machine Learning Engineer, you'll develop scalable ML solutions using Google Cloud Platform and Vertex AI, collaborate with teams, and operationalize models from ingestion to monitoring.

Lead architecture, design, and delivery of enterprise-scale Agentic AI solutions. Oversee multiple engineering pods, mentor teams, manage client-facing architecture discussions, ensure production-grade MLOps, cloud infrastructure, microservices, observability, and adoption of emerging AI technologies.