Staff AI Backend Engineer (f/m/d)
paretos GmbH
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Compensation
Salary & market context
Salary not listed
Requirements
Top requirements
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- 8+ years as a Backend, Software, or AI Engineer in a product-oriented environment , with a track record of leading the design and delivery of complex systems — ideally in a product or SaaS context ; startup (0→1) experience very welcome.
- Larger parts of a tech organization have been yours end-to-end: significant services, domains, or platform areas — from architecture and delivery to operation, reliability, and on-call , not just individual features.
- Deep backend expertise across Python (FastAPI, Pydantic), clean and durable API design, async programming, PostgreSQL and Redis, Docker, and mature CI/CD — at a level where you define standards rather than follow them.
Perks & setup
Benefits candidates care about
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- You receive the hardware setup you need to unlock your full potential.
- You use your personal learning budget (2000€ per year) to continuously develop yourself.
- Flexible working hours: you work when it suits you best.
Why candidates care
Benefits & perks
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- You receive the hardware setup you need to unlock your full potential.
- You use your personal learning budget (2000€ per year) to continuously develop yourself.
- Flexible working hours: you work when it suits you best.
- You receive a monthly benefit budget of 60€ that you can use however fits you best — whether that's Urban Sports Club, a mobility budget for fuel, public transport or the Deutschlandticket, or simply gift cards from your favorite shop. It's entirely up to you.
- A rich selection of snacks and drinks is available to you free of charge in the office at all times.
- You get direct access to frontier AI developments and learning sessions in close exchange with our product development.
- You complement a highly qualified and motivated team of paretoneers who get things done and have fun doing it.
Start here
Requirements
- s
- 8+ years as a Backend, Software, or AI Engineer in a product-oriented environment , with a track record of leading the design and delivery of complex systems — ideally in a product or SaaS context ; startup (0→1) experience very welcome.
- Larger parts of a tech organization have been yours end-to-end: significant services, domains, or platform areas — from architecture and delivery to operation, reliability, and on-call , not just individual features.
- Deep backend expertise across Python (FastAPI, Pydantic), clean and durable API design, async programming, PostgreSQL and Redis, Docker, and mature CI/CD — at a level where you define standards rather than follow them.
- Proven in agile, fast-moving teams : operating inside sprints, iterative shipping, and continuous feedback , you keep quality high while moving fast .
- Demonstrated technical leadership : you've owned the architecture of non-trivial systems , made high-stakes trade-offs , and guided multiple engineers through ambiguity — without formal authority .
- A strong product mindset drives you: not just "How do I build this?" but "What for, for whom, and how will we know it has impact?" — thinking in users, adoption, and chains of impact .
- Production-grade LLM and agent experience : prompt engineering, tool use/function calling, RAG, eval frameworks, guardrails , and deploying LLM-powered features real users depend on .
Responsibilities
What you'll do
- Tasks As a Staff AI Backend Engineer, you own the technical backbone of our Decision Intelligence platform and the architecture that lets it scale.
- You set the direction for how we build scalable backend services that bring our forecasting and optimization models, agentic workflows, and our semantic data layer into productive use.
- Scalable backend services in Python (FastAPI, Pydantic) for agent workflows, forecasting, and replenishment are yours to architect, build, and operate — including the decisions, trade-offs, and standards behind them.
- Through code and design review, mentoring, documentation, and standards , you raise the engineering bar — growing the people around you and making the system more resilient over time.
- Requirements 8+ years as a Backend, Software, or AI Engineer in a product-oriented environment , with a track record of leading the design and delivery of complex systems — ideally in a product or SaaS context ; startup (0→1) experience very welcome.
- Deep backend expertise across Python (FastAPI, Pydantic), clean and durable API design, async programming, PostgreSQL and Redis, Docker, and mature CI/CD — at a level where you define standards rather than follow them.
Role snapshot
About the role
paretos is the leading AI-based Decision Intelligence platform for effective, data-driven decisions across entire enterprises. Our Business Agents make it possible to evaluate complex data volumes, predict scenarios, and derive optimal actions - from demand forecasting and replenishment to strategic C-level decisions. All through a code-free interface, with no prior data science knowledge required. paretos is an inclusive company with equal opportunities for all. Our culture is guided by the principles of GUNG HO : meaningful work, shared goals, and mutual support. We are proud of a team of passionate, high-performing, and caring people of all genders, cultures, and backgrounds.
As Staff AI Backend Engineer, you work in a remote or hybrid setup in close collaboration with Product, Forward Deployed/AI Engineers, and Data Scientists. You operate as the technical anchor for our backend and agent platform — setting direction, raising the bar, and multiplying the impact of the engineers around you. We look forward to your application.
Tasks
As a Staff AI Backend Engineer, you own the technical backbone of our Decision Intelligence platform and the architecture that lets it scale. You set the direction for how we build scalable backend services that bring our forecasting and optimization models, agentic workflows, and our semantic data layer into productive use. You work AI-first: Claude Code or similar CLIs, MCPs, and custom skills are your standard toolkit, and you define how the rest of the team uses them. You think not only in terms of endpoints and databases, but in customer value, product impact, and adoption - and you are relentless about strengthening the system, not just solving the task at hand.
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