Software Engineer - Context and Semantics (AI)

Workday•Pleasanton, CA

8d•Hybrid

About The Position

The Data Cloud organization is at the forefront of Workday’s next major wave of innovation, driven by a bold mandate to build the industry’s first true Platform for Agents. We are a high-energy, ambitious team that balances Workday's deep foundational expertise with fresh, forward-thinking perspectives to accelerate our pace of delivery and unlock massive new commercial value. By leveraging Workday’s unique business context, trusted data, and identity guardrails, we are building the critical engine that will power the future of AI and agentic workflows across the enterprise. Within this organization, the Context and Semantics Engineering team acts as the brain trust responsible for making our AI "enterprise-aware." We build the semantic layers, knowledge graphs, query federation engines, and dynamic context systems that ground generative AI in real-world HR and Finance data. By bridging the gap between raw enterprise data and large language models (LLMs), our team ensures that Workday’s autonomous agents can reason accurately, securely, and with deep contextual awareness at a global scale. As a Software Development Engineer in Context and Semantics, you will be a core backend builder helping to develop the data and context infrastructure powering Workday’s AI Agents. Working alongside senior engineers and technical leads, you will write clean, scalable code that connects large language models to complex enterprise data stores. In this role, you will implement components for advanced RAG (Retrieval-Augmented Generation) pipelines, integrate vector databases, and help build query federation services (e.g., Trino/LDQ) that route requests between transactional and analytical systems. We are looking for an energetic, curious, and detail-oriented backend software engineer who is eager to solve complex data challenges and build cutting-edge AI platform features at scale.

Requirements

3+ years of professional software engineering experience focusing on backend development, microservices, or data systems.
2+ years of strong hands-on programming skills in Java, Python, Scala, or Go.
1+ year of demonstrated coursework, personal projects, or professional exposure to AI/ML concepts, LLM APIs, Vector Databases, or RAG architectures.
Solid foundation in computer science fundamentals: data structures, algorithms, distributed systems concepts, and API design (REST/gRPC).
Familiarity with SQL, relational databases, or distributed data processing engines (e.g., Trino, Presto, Spark).
Understanding of modern cloud infrastructure (AWS or GCP) and CI/CD development workflows.
Fast learner with strong problem-solving skills and a passion for staying up-to-date with emerging AI technologies.
Strong verbal and written communication skills with a team-first, collaborative mindset.

Nice To Haves

Bachelor’s degree (Master’s preferred) in Computer Science, Software Engineering, or an equivalent technical field.

Responsibilities

Design, develop, and test scalable backend microservices, APIs, and data pipelines powering the context and semantic layer for AI Agents.
Help build and maintain enterprise RAG pipelines, including embedding workflows, document chunking strategies, and vector database integrations (e.g., Pinecone, Milvus, Elasticsearch).
Support the development of distributed query execution features (leveraging technologies like Trino) to retrieve enterprise data securely for LLMs.
Implement orchestration logic and context-management components using modern AI frameworks (e.g., LlamaIndex, LangChain) to support multi-turn agent interactions.
Ensure all code adheres to strict enterprise standards around data privacy, tenant isolation, and Role-Based Access Control (RBAC).
Write thorough unit and integration tests, participate in code reviews, and help maintain high system reliability, low latency, and robust operational monitoring.
Partner closely with Senior Engineers, Product Managers, and team leads to break down technical requirements into well-executed code deliverables.