Software Development Engineer
Explicitly mentions vibe coding and building agentic LLM/RAG workflows—directly tied to vibe coding and AI assistant development.
About the Role
Workday is hiring an AI/ML-focused Software Development Engineer to design and build production-grade, agentic AI workflows for Adaptive Planning that improve financial planning for enterprise customers. The role combines ML engineering, software development, and product-minded design to embed scalable, secure AI capabilities into a SaaS planning platform.
Job Description
Role
Workday Adaptive Planning is hiring an AI/ML Engineer to design and build intelligent, agentic workflows that enhance financial planning at enterprise scale. The role focuses on bringing AI-first solutions into production and embedding secure, scalable agentic capabilities into core planning features.
Key Responsibilities
- Design and develop sophisticated AI agents that reason, learn, and interact across complex business processes.
- Embed secure, scalable, and reliable agentic capabilities into Adaptive Planning features.
- Own projects across the development lifecycle: problem framing, data preparation, design, development, deployment, evaluation, and continuous improvement.
- Collaborate closely with engineers and product managers to deliver customer-focused solutions.
- Provide technical guidance and mentorship to junior engineers and conduct critical code reviews.
Requirements
Basic Qualifications
- 2+ years on a data science, machine learning, or related software development team.
- 2+ years using Python with ML/AI libraries for production solutions.
- 3+ years of object-oriented programming experience in Java.
- 3+ years of SaaS software development experience.
Other Qualifications
- Experience with large language models (LLMs), retrieval-augmented generation (RAG), semantic search, and text embedding models.
- Familiarity with vibe coding, MCP, LangGraph, and transformer neural networks.
- Experience with cloud platforms (e.g., AWS, GCP), containerization (e.g., Docker), and data engineering/ETL pipelines.
- Strong focus on test automation, performance engineering, and delivering high-quality production software.
- Experience deploying machine learning solutions at scale, including data collection, labeling, model development, validation, deployment, and monitoring.
- Product mindset, strong problem-solving skills, and ability to balance speed with quality.
Work Model
- Flexible hybrid approach: teams spend at least 50% of their time each quarter in the office or in the field. Remote “home office” roles are supported with opportunities to come together for key moments.