Principal SDE, Agentic AI Engineering

About the Role

You will play a key part in designing and developing the future localisation and agentic AI capabilities for Workday to achieve our language experience strategies by leveraging the power of AI. You will lead by example, demonstrating architecture and product development best practices, while mentoring and empowering your fellow team members to achieve success.

Building Agentic AI solutions : You will guide the creation and implementation of sophisticated AI agents, including finetuning models and developing complex agentic RAG solutions to address complex business challenges and enhance our products.

Mentoring and guiding engineers : You will provide technical leadership and mentorship to a team of hard-working ML engineers, fostering a culture of innovation and teamwork.

Collaborating with product and engineering : You'll partner with product managers and engineering teams to define product roadmaps and ensure successful integration of AI solutions.

Staying ahead of the curve : You will keep yourself informed of the latest advancements in machine learning and AI research, identifying and evaluating new technologies for potential adoption.

About You

Basic Qualifications

12+ years of experience with product engineering leading the development and delivery SaaS products, including familiarity with Java, full-stack, devops and related technologies and best practices

8+ years experience in Python, LangChain / LangGraph and supporting numeric libraries, with experience in shipping production code and models

An ability to balance a sense of urgency with delivering high-quality, practical solutions. Proven perseverance in overcoming challenging problems.

Other Qualifications

Bachelor's degree in a relevant field, such as Computer Science, Mathematics, or Engineering.

Practical experience with generative models, large language models (LLM), retrieval augmented generation (RAG) systems, transformer neural networks.

Experience with cloud computing platforms (e.g. AWS, GCP), containerization technologies (e.g. Docker) and data engineering pipelines (e.g. ETL)

Experience developing and deploying machine learning solutions using large-scale datasets, including specification design, data collection and labeling, model development, validation, deployment, and ongoing monitoring.

Experience with finetuning models including identifying and curating datasets as well as experimenting with models for iterative improvement