Hybrid role in Pleasanton, CA with a focus on building next-generation AI platforms for enterprise billing integrity across global regions. On Workday’s Business Process Optimize team within Admin Central, you will lead the architecture and implementation of agentic capabilities, scalable ML infrastructure, and high-availability usage pipelines, partnering with cross-functional teams to turn complex, ambiguous business needs into production-ready systems.
Compensation: USD 190,600 - 342,000 per year. Workday’s pay transparency notes the primary base pay range for the USA.CA.Pleasanton location as $228,000 - $342,000, with additional US location(s) base pay range of $190,600 - $342,000.
How you’ll contribute
- Design, implement, and maintain agentic capabilities including tool calling, multi-agent workflows, long-range planning, reasoning frameworks, and human-in-the-loop integrations.
- Build secure, high-throughput REST services and event-driven data pipelines using Python, Kubernetes, and cloud-native technologies.
- Architect multi-tenant runtimes to deliver fast, low-latency inference to millions of global users.
- Establish end-to-end MLOps standards across CI/CD automation, observability, evaluation frameworks, model monitoring, and privacy/anonymization guardrails, keeping systems explainable, secure, and production-resilient.
- Partner with software engineers, Product Managers, Data Engineering, SRE, and Legal/Compliance stakeholders to shape scalable technical designs and long-term data-governance strategies.
What you bring
- 12+ years in ML development and R&D leadership, including 8+ years with advanced ML methodologies, distributed frameworks, and cloud-based platforms, designing complex scalable production ML systems.
- 6+ years building and hosting scalable ML services, microservices, and high-throughput data pipelines in production using Java, Go, Scala, or Python.
- 4+ years working with LLMs, generative AI, RAG pipelines, and autonomous agent frameworks for real-world enterprise applications.
- Expertise in high-throughput streaming with Kafka, real-time feature extraction with Spark/Flink, and distributed columnar storage using ClickHouse and Iceberg.
- Deep knowledge of deep learning, LLM orchestration, RAG, and agentic systems, applying ML algorithms and Model-Based Design including MCP integrations and tool-use frameworks to multi-agent coordination, intent classification, and workflow optimization.
- Proven ability to lead EDA for multi-tenant agentic workflows, with mastery of high-dimensional embeddings, vector database management, and data isolation.
- Proven ability to lead model building, fine-tuning, and deployment of domain-specific foundation models, including Kubernetes/Docker deployment, automated CI/CD retraining, and managing drift/latency at scale.
- Mastery of Python and the ML ecosystem, with experience deploying production systems on PostgreSQL, Redis, Kafka, and cloud infrastructure (AWS, GCP, Azure).
- Exceptional collaboration and mentoring skills, including leading cross-functional pods, driving sprint planning, and translating technical and governance decisions for executives.
- Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or related field (or equivalent experience); Master’s or PhD strongly preferred.
Tech you’ll work with
- Agentic AI & LLMs: autonomous agent frameworks, tool calling, multi-agent workflows, long-range planning, reasoning frameworks, human-in-the-loop integrations, LLMs, generative AI, RAG pipelines, LangChain, LangSmith, LlamaIndex
- ML & deployment: PyTorch, TensorFlow, NumPy, Pandas, scikit-learn, Docker, Kubernetes
- Data & infrastructure: Kafka, Spark, Flink, ClickHouse, Iceberg, PostgreSQL, Redis, AWS/GCP/Azure
- MLOps & governance: CI/CD automation, observability, model monitoring, privacy/anonymization guardrails
- Design frameworks: MCP integrations, Model-Based Design, EDA, high-dimensional embeddings, vector database management
Flexible work approach
At least 50% of your time each quarter will be spent in the office or in the field with customers, prospects, and partners, depending on role. People in remote “home office” roles also have opportunities to come together in offices for important moments.
