AI Engineer (Agents)

Storytel

Stories have the power to make the ordinary extraordinary. They help us think bigger, see beyond ourselves, and deepen our connection with others. As one of the world's leading audiobook and e-book streaming platforms, Storytel brings unlimited listening to millions of users across 25 markets. Driven by our purpose, "Leading the future of storytelling, we move the world through stories", Storytel Group inspires and entertains people around the world by blending innovation with tradition. We bring stories to life across various formats for everyone to discover. Anytime. Anywhere. Ready for your next chapter? We're looking for an Agent Engineer to join our newly formed Agent Engineering team in Sweden! ABOUT THE TEAM Agent Engineering is a new team at Storytel with a clear mission: find where AI agents can create real leverage across the organisation, and then build them. We work closely with business functions, understand their problems, and help them automate workflows using AI agents in a way that actually works reliably. This isn't an R&D team and it isn't a consulting team. We sit at the intersection. When an agent is the right answer, we build one. When something else is the better solution, we say so. We build, test, evaluate, and iterate rather than ship things that only look like they work. The team is small by design (2-3 people). We operate as a sibling team to Storytel's Platform Engineering team, who own the scalable agent infrastructure. We focus on the agents themselves: understanding the problem, designing the solution, integrating the right data and tools, and iterating until it reliably delivers. If we get this right, we will fundamentally change how much of this organisation operates. ABOUT THE ROLE This is a software development role, but also a bit more than that. We call it an Agent Engineer, this role bridges business and technology: understanding real problems with stakeholders, translating them into agent architectures, and doing the integration work to connect everything together. As an Agent Engineer, you will work across the full lifecycle of agent development, from the first stakeholder conversation to a working, evaluated solution in production. You will be one of the first people to join this team, which means a high degree of ownership and a wide scope of work. You will work alongside our Agent Engineering Lead who also manage this team. On any given week, you might be mapping out a workflow problem with a business team, designing the architecture of an agent, or troubleshooting an integration to get the right data flowing to the right place. Blockers are a given in this kind of work, so you will typically have several projects on the go and move between them as needed. A key part of the role is building base agents that business teams can later adapt themselves, adjusting prompts, data sources, or configurations to tackle similar problems without starting from scratch each time. ABOUT YOU To be successful in this role, we believe that you have: * A strong engineering foundation. You have built real things, you understand what good architecture looks like, and you knew how to write software long before AI tools made it easier. * A practical instinct for the right tool. When a scripted solution is better than an LLM, you reach for the scripted solution. You understand the tradeoffs and can articulate them clearly. * Real experience with integration challenges. Getting the right data, with the right permissions, to the right place is often where the hard problems live. You have been there before. * The ability to translate across disciplines. You can sit with a business stakeholder, understand what is actually broken, and then turn around and have a completely different conversation with a platform engineer about API design. * Comfort with ambiguity and parallel workstreams. When you hit a wall, you work through it or find another route. You do not need a detailed brief to get started. * Pragmatic ambition. You would rather ship something that works and iterate than design something perfect that never ships. But pragmatic does not mean sloppy. * A willingness to speak up honestly. You say so when something is a bad idea, and you come with a better suggestion. You do not need to win every argument, but you are direct about your view. * At least 5 years of professional software engineering experience. * Hands-on experience with Python in a production context. Java is our primary backend language at Storytel, and Python is the language of choice for agent work. * Familiarity with Google Cloud Platform or similar cloud experience. * Prior exposure to agent frameworks or LLM tooling (we use Google ADK, but it is learnable if you have the foundations). JOIN A WORLD OF STORIES At Storytel, we're a team of creative story lovers who thrive on collaboration and new ideas. Our workplace is friendly, dynamic, and full of opportunities to experiment and make an impact. We believe in trust, flat hierarchies, and empowering you to grow with us. Does this sound like your next story? Simply fill out the application form and share your CV or LinkedIn profile. Answer a few questions, and you're all set. You have all of July to apply, we will start interviewing in August.

Sana

ABOUT SANA Sana is an AI lab building superintelligence for work. We believe organizations can accomplish their missions faster when teams can effortlessly access knowledge, automate repetitive work, and learn anything with the help of agentic AI. As part of Workday, we are committed to building AI that augments people - not replaces them. We bring this mission to life through two products. Sana Agents provide a seamless way to access all your company’s apps, knowledge, and data, enabling AI agents to do real work so teams can process and act on information at unprecedented scale. Sana Learn is an AI-powered learning hub that combines the simplicity of a modern learning platform with intelligent features like an AI tutor, automated content generation, and interactive apps, making knowledge not just accessible but actionable. We’re a talent-dense, product-obsessed team of engineers and designers from companies like Google, Spotify, Apple, and Databricks, united by deep technical excellence and rapid iteration. Our tools already help over a million people learn and work better across hundreds of leading enterprises - and we’re just getting started. ABOUT THE ROLE You’ll be the quality champion for Sana’s AI agent platform, ensuring our LLM-powered products are robust, reliable, and a delight to use. You’ll design and implement test strategies that keep pace with rapid iteration, automate critical workflows, and drive a culture of quality across engineering. This is a hands-on role for someone who thrives on constructing scalable ways of breaking things, uncovering edge cases unique to agentic and LLM-based systems, and building the safeguards that prevent issues from reaching production. You’ll help us deliver agent workflows that are safe, trustworthy, and enterprise-ready, for the AI landscape of today and tomorrow. IN THIS ROLE, YOU WILL * Design and implement test plans for agent infrastructure, LLM-based APIs, and end-to-end user journeys * Build and maintain automated test suites for backend, frontend, and integration layers, including prompt and response validation for generative models * Develop tools and frameworks to accelerate testing and catch regressions early, especially in agent reasoning, tool use, and context handling * Collaborate closely with engineers to embed quality into every stage of development, with a focus on the unique challenges of AI/LLM systems (e.g., non-determinism, hallucinations, safety) * Lead root cause analysis and drive resolution for critical issues and incidents, including those arising from model updates or agent behaviors * Advocate for best practices in code quality, observability, and CI/CD pipelines—ensuring quality signals are actionable and visible WHAT SUCCESS LOOKS LIKE * Critical bugs, regressions, and model failures are caught before they reach users—even as we scale and ship rapidly * Automated test coverage is high, reliable, and easy to maintain, including for LLM outputs and agent workflows * Release cycles are fast and safe. Confidence in shipping is high, even with evolving models and agent capabilities * Quality metrics (including model quality, agent reliability, and user experience) and dashboards provide clear, actionable signals to the team * You are a go-to partner for engineers, raising the bar for quality and reliability in AI-driven systems OUR TECH STACK We build on a simple modern stack optimized for both humans and AI. * Backend: TypeScript, Kotlin, Node.js * Frontend: TypeScript, React, Tailwind * Databases: Postgres, Redis * Cloud infra: GCP/Kubernetes/Terraform WHAT WE OFFER * Help shape AI's future alongside brilliant minds from Google, Apple, Spotify, Notion, Slack, Databricks, and BCG. * Competitive salary complemented with a competitive RSU program. * Swift professional growth in an evolving environment, supported by a culture of continuous feedback and mentorship from senior leaders. * Work with talented teammates across 5+ countries, and global customers, from our beautiful office in Stockholm.

Workday Sweden Aktiebolag

Sana is an AI lab building superintelligence for work. We believe organizations can accomplish their missions faster when teams can effortlessly access knowledge, automate repetitive work, and learn anything with the help of agentic AI. As part of Workday, we are committed to building AI that augments people - not replaces them. We bring this mission to life through two products. Sana Agents provide a seamless way to access all your company’s apps, knowledge, and data, enabling AI agents to do real work so teams can process and act on information at unprecedented scale. Sana Learn is an AI-powered learning hub that combines the simplicity of a modern learning platform with intelligent features like an AI tutor, automated content generation, and interactive apps, making knowledge not just accessible but actionable. We’re a talent-dense, product-obsessed team of engineers and designers from companies like Google, Spotify, Apple, and Databricks, united by deep technical excellence and rapid iteration. Our tools already help over a million people learn and work better across hundreds of leading enterprises - and we’re just getting started. About the Role You'll build the core agent infrastructure that powers Sana's mission to bring superintelligence to work. This is a greenfield opportunity to define how AI agents plan, reason, and execute across enterprise environments—building systems that reliably handle real-world complexity at scale. You'll work at the intersection of agent architecture, context-, tool- and prompt engineering, and production infrastructure. In this role, you will Architect multi-step planning, orchestration, and tool routing for agents Implement code generation agents and sandboxed code execution Engineer memory, state, and context packing/grounding strategies Balance latency, quality, and cost controls for agent execution Develop safe fallbacks, graceful degradation and robust error handling Collaborate with platform and search teams to deliver reusable agent infrastructure Establish safety guarantees and measurable quality improvements About You Basic Qualifications: 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. Other Qualifications: 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. Contributions to open source TypeScript projects. Ability to navigate ambiguity, make strong technical tradeoffs, and drive projects from concept to production. Strong communication and collaboration skills, with the ability to partner effectively across engineering, product, and AI research teams.