Senior AI Engineer - Sana

Workday Workday · Enterprise · Stockholm, Sweden +1

Senior AI Engineer role focused on building the core agent infrastructure for Workday's Sana AI lab. The role involves architecting multi-step planning, orchestration, and tool routing for agents, implementing code generation and execution, engineering memory and context strategies, and balancing performance with cost. The position requires experience with LLM-powered applications, agent platforms, and production ML systems.

What you'd actually do

  1. Architect multi-step planning, orchestration, and tool routing for agents
  2. Implement code generation agents and sandboxed code execution
  3. Engineer memory, state, and context packing/grounding strategies
  4. Balance latency, quality, and cost controls for agent execution
  5. Develop safe fallbacks, graceful degradation and robust error handling

Skills

Required

  • software engineering experience building production backend or platform systems
  • TypeScript
  • distributed systems
  • APIs
  • asynchronous workflows
  • service-oriented architecture
  • scalability
  • reliability
  • observability
  • maintainability

Nice to have

  • building and deploying LLM-powered applications in production
  • building agent platforms or AI infrastructure
  • low-level details of OpenAI, Google, and Anthropic LLM APIs
  • tool calling
  • system prompt caching
  • LLM application patterns
  • retrieval-augmented generation (RAG)
  • memory and context management
  • multi-step orchestration
  • human-in-the-loop systems
  • building and running machine learning systems in production
  • compiling training and test datasets
  • building training pipelines
  • evaluating models
  • detecting and handling drift
  • designing evaluation frameworks for LLM or agent quality and safety
  • Langfuse
  • LangSmith
  • vector databases
  • prompt engineering
  • context engineering
  • experimentation tooling
  • sandbox environments (Modal)
  • strict access control models
  • Kubernetes
  • GCP
  • Postgres
  • Redis
  • high-throughput, low-latency service contexts
  • contributions to open source TypeScript projects
  • navigating ambiguity
  • making strong technical tradeoffs
  • driving projects from concept to production
  • communication and collaboration skills

What the JD emphasized

  • 3+ years of software engineering experience building production backend or platform systems
  • 3+ years of experience in TypeScript, with a strong track record of writing reliable, maintainable services
  • 3+ years of experience with distributed systems, APIs, asynchronous workflows, and service-oriented architecture
  • 3+ years of experience designing systems with a focus on scalability, reliability, observability, and maintainability
  • Experience building and deploying LLM-powered applications in production
  • Experience building agent platforms or AI infrastructure
  • Deep understanding of the low-level details of the OpenAI, Google, and Anthropic LLM APIs, including tool calling, system prompt caching, etc.
  • Familiarity with LLM application patterns, including tool calling, retrieval-augmented generation (RAG), memory and context management, multi-step orchestration, and human-in-the-loop systems
  • Experience building and running machine learning systems in production, including compiling training and test datasets, building training pipelines, evaluating models, and detecting and handling drift (neural networks, Gaussian models, Thompson sampling, etc.)
  • Experience designing evaluation frameworks for LLM or agent quality and safety, including hands-on use of platforms such as Langfuse or LangSmith
  • Familiarity with vector databases, prompt and context engineering, and experimentation tooling
  • Experience working with sandbox environments such as Modal, and designing strict access control models to keep user data safe and encrypted at all times
  • Experience running services in Kubernetes-based environments on GCP or equivalent cloud platforms
  • Comfort working with Postgres and Redis in high-throughput, low-latency service contexts
  • Ability to navigate ambiguity, make strong technical tradeoffs, and drive projects from concept to production

Other signals

  • building agent infrastructure
  • multi-step planning and orchestration
  • production LLM applications