W
Workday
AI Engineer/Senior AI Engineer (Evisort)
Skills
Google Cloud PlatformDeep LearningDockerReactTypescriptDjangoPythonProduct ManagementData SciencePyTorchTensorFlowPostgreSQLKafkaRedisDatadogGrafanaMachine LearningAIAWSPrometheusKubernetesWorkdayFastAPITerraform
2 months ago
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Important information
Contract type:
Permanent
Location:
Canada
Work mode:
Onsite
Published on:
29 August 2026
What they need
- As an AI Engineer, you will help develop tailored user experiences using advanced LLMs, Knowledge Graphs, personalization, and predictive analysis
- You will collaborate with other engineers to deliver AI solutions across Workday’s product ecosystem and utilize software and data engineering stacks to enable training, deployment, and lifecycle management of various AI pipelines
- You will develop and deploy new products at scale and leverage Workday’s vast computing resources on rich datasets to deliver transformative value to our customers
- In addition to contributing to feature and service development, you must have an approach of continuous improvement, passion for quality, scale, and security
- You must be curious and prepared to question or challenge choices and practices where they don’t make sense to you or could be improved
- You also should have a product approach and strong intuition around how AI can drive a better customer experience
- Lastly, a strong sense of ownership and teamwork are essential to succeed in this role- 5+ years experience as a member of a data science, machine learning engineering, or other relevant software development team building applied machine learning products at scale, including taking products through applied research, design, implementation, production, and production-based evaluation
- 2+ years of professional experience in building services to host machine learning models in production at scale
- 2+ years of demonstrated experience working with large language models (LLMs), text generation models, and/or graph neural network models for real-world use cases
- Bachelor’s (Master’s or PhD preferred) degree in engineering, computer science, physics, math or equivalent
- 2+ years of proven experience with cloud computing platforms (e.g. AWS, GCP, etc.)
- 2+ years of professional experience in machine learning and deep learning frameworks & toolkits such as Pytorch, TensorFlow
- 4+ years of proven experience leveraging cloud computing platforms (e.g., AWS, GCP) to deploy highly responsive, scalable systems
- Bachelor’s degree (Master’s preferred) in Computer Science, Software Engineering, or equivalent technical field
- System Design & Reusability: Proven ability to architect robust application layers that wrap around AI models, establishing reusable patterns for system predictability, error handling, and seamless UX integration
- 4+ years of experience optimizing application performance, specifically tackling product constraints such as API latency, token management, and user interaction design
- Familiarity with data persistence and async processing - relational databases and schema migrations (e.g., PostgreSQL), caching (e.g., Redis), and message/queue or task workflows (e.g., Celery, Kafka, SQS)
- Collaborative Communication: Excellent interpersonal skills, with a knack for bridging the gap between product management, design, and foundational ML infrastructure teams
- Technical Leadership & Mentorship: Proven track record of technically leading engineering workstreams, taking ownership of the development lifecycle, and mentoring junior-to-mid level engineers
- 8+ years of professional experience in software engineering and product-building, with a proven track record of shipping and maintaining production code at scale
- Hands-on experience with production observability and monitoring tooling (e.g., Prometheus/Grafana, OpenTelemetry, Sentry, Datadog) to debug, trace, and measure live systems
- Thrives in Ambiguity: Highly autonomous builder capable of taking open-ended product goals and breaking them down into concrete, scalable engineering realities
- Experience using feature-flag/experimentation platforms (e.g., LaunchDarkly) to safely roll out, A/B test, and decommission changes, including flag lifecycle and cleanup discipline
- 1–2+ years of hands-on experience building with modern AI orchestration frameworks (e.g., LangChain, LlamaIndex, or multi-agent frameworks) and implementing advanced prompt engineering techniques to manage complex agentic workflows
- 3+ years of hands-on experience integrating large models (LLMs, Foundation Models) and modern AI APIs into user-facing enterprise products
- Hands-on experience deploying and operating services in containerized, cloud-native environments (Docker, Kubernetes), with exposure to infrastructure-as-code and GitOps tooling (e.g., Terraform, Helm, ArgoCD)
- Experimentation & Evaluation: Skilled in rapid prototyping, benchmarking model outputs against product requirements, and setting up automated evaluation metrics (e.g., assessing retrieval quality and agentic behavior)
- Familiarity with full-stack development - building web UIs (e.g., React, TypeScript) and backend services/REST APIs in Python (e.g., FastAPI, Django)
- Other Qualifications
- Product-First AI Mindset: Deep focus on business value, user experience, and applying deep learning or large models directly to solve practical end-user challenges
- A track record of writing well-tested, maintainable production code with attention to code quality, security scanning, and dependency hygiene (e.g., unit/integration tests, SonarQube, dependency tooling)
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