Principal Machine Learning Engineer, Business Process Optimization

As a Principal Machine Learning Engineer on the Business Process Optimize team within Admin Central, you will lead the architecture and implementation of Workday's next-generation AI platforms and autonomous agent frameworks. You'll sit at the intersection of machine learning, event-driven data systems, and enterprise platform engineering - transforming cutting-edge AI research into production-ready, highly reliable systems handling tens of millions of daily requests for over 100+ Tier-0 enterprise tenants.

In this hands-on, high-impact role, you will design the ML infrastructure, multi-agent orchestration layers, and high-availability usage pipelines that power enterprise-grade decision-making, usage tracking, and billing integrity across international regions.

Your Primary Focus:

• Agentic AI & Orchestration: Design, implement, and maintain advanced agentic capabilities, including tool calling, multi-agent workflows, long-range planning, reasoning frameworks, and human-in-the-loop integrations.
• Scalable ML Infrastructure: Build secure, high-throughput REST services and event-driven data pipelines using Python, Kubernetes, and cloud-native tech. Architect multi-tenant runtimes that deliver fast, low-latency inference to millions of global users.
• MLOps & Enterprise Reliability: Establish end-to-end MLOps standards - including CI/CD automation, observability, evaluation frameworks, model monitoring, and privacy/anonymization guardrails - ensuring systems are explainable, secure, and production-resilient.
• Technical & Strategic Leadership: Partner closely with software engineers, Product Managers, Data Engineering, SRE, and Legal/Compliance stakeholders to turn ambiguous business needs into scalable technical designs and long-term data-governance strategies.

• 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-grade 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.

• Data Processing & Pipelines: Expert in high-throughput streaming (Kafka), real-time feature extraction (Spark/Flink), and distributed columnar storage (ClickHouse, Iceberg) to architect large-scale ML pipelines.
• AI/ML Expertise: 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.
• Data Analysis & Feature Engineering: Proven ability to lead EDA on multi-tenant agentic workflows to shape data strategy, paired with mastery of high-dimensional embeddings, vector database management, and data isolation.
• Model Lifecycle: 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.
• Technical Stack: Mastery of Python and the ML ecosystem (PyTorch, TensorFlow, LangChain, LangSmith, LlamaIndex, NumPy, Pandas, Scikit-learn), with strong experience deploying production systems on PostgreSQL, Redis, Kafka, and cloud infrastructure (AWS/GCP/Azure).
• Team Leadership: Exceptional collaboration and mentoring skills - technically leading cross-functional pods, driving sprint planning, and translating technical/governance decisions for executives.
• Education: Bachelor's in Computer Science, Data Science, Statistics, Mathematics, Engineering, or related field (or equivalent experience); Master's or PhD strongly preferred.