Full job description

Your tasks

As a Staff AI Backend Engineer, you are responsible for the technical backbone of our decision intelligence platform and the architecture that makes it scalable. You define the direction for building scalable backend services that make our forecasting and optimization models, agent-based workflows, and semantic data layer productively usable. You work AI-first: Claude Code or comparable CLIs, MCPs, and your own skills are your standard toolkit – and you determine how the rest of the team uses them. You think not only in terms of endpoints and databases, but also in terms of customer value, product impact, and adoption – and you consistently strengthen the system rather than just solving the individual task.

• With a strong product mindset, you set the technical direction: You define how we measure feature adoption, instrument usage patterns, and manage the roadmap together with Product and FDE – responsible for outcomes, not output.
• Scalable backend services in Python (FastAPI, Pydantic) for agent workflows, forecasting and replenishment are in your hands: You architect, build and operate them – including the decisions, trade-offs and standards behind them.
• You will design clean API and service contracts between the platform, agents, ML models, frontend and customer systems (ERP, BI, planning tools) – with robustness, observability and reliability as top-tier properties.
• Our semantic data layer – the foundation for precise forecasts and accurate agent responses – is yours: PostgreSQL modeling, dbt transformations, pipelines, caching and retrieval, built to scale across customers and use cases.
• You define how we integrate LLM and agent systems into the backend: Tool Use, Function Calling, RAG Pipelines, Eval Frameworks, Guardrails and Production Deployment – ​​you set the patterns and quality standards against which the team builds.
• Driving our AI-first engineering culture forward is central: Claude Code, AI-powered development and subagents are part of your daily toolkit, and you further develop our internal setup (CLAUDE, Hooks, Skills, Plan/Execute/Review loop, Plugins, Docs) so that the team delivers faster and more securely.
• Together with FDEs and Product, you translate ambiguous business and customer requirements into a coherent technical strategy and make pragmatic trade-offs between speed, scalability and maintainability – clearly explained for both technical and non-technical stakeholders.
• You prototyp quickly and closely with the user, get ideas into the hands of FDEs and customers within days, independently gather feedback, iterate on the real workflow and decide what gets hardened and rolled out based on actual usage.
• Through code and design reviews, mentoring, documentation and standards, you raise the engineering level – you develop the people around you and make the system more resilient over time.

Your profile

8+ years as a backend, software, or AI engineer in a product-oriented environment, with demonstrable experience leading the design and delivery of complex systems – ideally in a product or SaaS context; startup experience (0

• 1) Very welcome.
• Large parts of a tech organization were in your hands end-to-end: significant services, domains, or platform areas – from architecture and delivery to operation, reliability, and on-call support, not just individual features.
• Deep backend expertise in Python (FastAPI, Pydantic), clean and durable API design, asynchronous programming, PostgreSQL and Redis, Docker and mature CI/CD – at a level where you define standards instead of following them.
• Proven in agile, fast teams: Through sprints, iterative shipping and continuous feedback, you maintain high quality while delivering quickly.
• Proven technical leadership: You have been responsible for the architecture of non-trivial systems, made consequential trade-offs, and guided several engineers through ambiguity – without formal authority.
• A strong product-oriented mindset drives you: not just "How do I build this?", but "What for, for whom and how do we know it has an effect?" – thinking in terms of users, adoption and impact chains.
• Production-grade LLM and agent experience: Prompt engineering, tool use/function calling, RAG, evaluation frameworks, guardrails, and the deployment of LLM features that real users rely on.
• Confident handling of cloud infrastructure (ideally AWS), including Kubernetes and Infrastructure-as-Code with Terraform – you design, penetrate and operate this stack confidently and decide where complexity belongs.
• A very high degree of ownership, sovereignty in dealing with ambiguity, and the judgment to know when to delve into the details and when to delegate, document, or simplify.
• Clear, convincing communication, both technical and non-technical, with little ego – you create alignment, document things in writing and make others better.
• Fluent English
• 
  

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Full job description

Your tasks

As a Staff AI Backend Engineer, you are responsible for the technical backbone of our decision intelligence platform and the architecture that makes it scalable. You define the direction for building scalable backend services that make our forecasting and optimization models, agent-based workflows, and semantic data layer productively usable. You work AI-first: Claude Code or comparable CLIs, MCPs, and your own skills are your standard toolkit – and you determine how the rest of the team uses them. You think not only in terms of endpoints and databases, but also in terms of customer value, product impact, and adoption – and you consistently strengthen the system rather than just solving the individual task.

• With a strong product mindset, you set the technical direction: You define how we measure feature adoption, instrument usage patterns, and manage the roadmap together with Product and FDE – responsible for outcomes, not output.
• Scalable backend services in Python (FastAPI, Pydantic) for agent workflows, forecasting and replenishment are in your hands: You architect, build and operate them – including the decisions, trade-offs and standards behind them.
• You will design clean API and service contracts between the platform, agents, ML models, frontend and customer systems (ERP, BI, planning tools) – with robustness, observability and reliability as top-tier properties.
• Our semantic data layer – the foundation for precise forecasts and accurate agent responses – is yours: PostgreSQL modeling, dbt transformations, pipelines, caching and retrieval, built to scale across customers and use cases.
• You define how we integrate LLM and agent systems into the backend: Tool Use, Function Calling, RAG Pipelines, Eval Frameworks, Guardrails and Production Deployment – ​​you set the patterns and quality standards against which the team builds.
• Driving our AI-first engineering culture forward is central: Claude Code, AI-powered development and subagents are part of your daily toolkit, and you further develop our internal setup (CLAUDE, Hooks, Skills, Plan/Execute/Review loop, Plugins, Docs) so that the team delivers faster and more securely.
• Together with FDEs and Product, you translate ambiguous business and customer requirements into a coherent technical strategy and make pragmatic trade-offs between speed, scalability and maintainability – clearly explained for both technical and non-technical stakeholders.
• You prototyp quickly and closely with the user, get ideas into the hands of FDEs and customers within days, independently gather feedback, iterate on the real workflow and decide what gets hardened and rolled out based on actual usage.
• Through code and design reviews, mentoring, documentation and standards, you raise the engineering level – you develop the people around you and make the system more resilient over time.

Your profile

8+ years as a backend, software, or AI engineer in a product-oriented environment, with demonstrable experience leading the design and delivery of complex systems – ideally in a product or SaaS context; startup experience (0

• 1) Very welcome.
• Large parts of a tech organization were in your hands end-to-end: significant services, domains, or platform areas – from architecture and delivery to operation, reliability, and on-call support, not just individual features.
• Deep backend expertise in Python (FastAPI, Pydantic), clean and durable API design, asynchronous programming, PostgreSQL and Redis, Docker and mature CI/CD – at a level where you define standards instead of following them.
• Proven in agile, fast teams: Through sprints, iterative shipping and continuous feedback, you maintain high quality while delivering quickly.
• Proven technical leadership: You have been responsible for the architecture of non-trivial systems, made consequential trade-offs, and guided several engineers through ambiguity – without formal authority.
• A strong product-oriented mindset drives you: not just "How do I build this?", but "What for, for whom and how do we know it has an effect?" – thinking in terms of users, adoption and impact chains.
• Production-grade LLM and agent experience: Prompt engineering, tool use/function calling, RAG, evaluation frameworks, guardrails, and the deployment of LLM features that real users rely on.
• Confident handling of cloud infrastructure (ideally AWS), including Kubernetes and Infrastructure-as-Code with Terraform – you design, penetrate and operate this stack confidently and decide where complexity belongs.
• A very high degree of ownership, sovereignty in dealing with ambiguity, and the judgment to know when to delve into the details and when to delegate, document, or simplify.
• Clear, convincing communication, both technical and non-technical, with little ego – you create alignment, document things in writing and make others better.
• Fluent English