AI Engineer (m/f/d), Junior to Senior

This is not a single-discipline ML job, and it is not a deep-learning job. You move across discrete-event simulation, Bayesian optimization and calibration, scheduling and MRP heuristics, and agentic LLM orchestration, often inside the same project. Client contact is rare by design: this is a modelling and optimization role rather than a delivery role. When it does happen, it is a workshop with a client stakeholder or with one of our academic research partners, not day-to-day account management.

• Python throughout, with FastAPI for services and PostgreSQL, SQLAlchemy and Alembic underneath.
• Our own discrete-event simulation engine, built in-house. Around fourteen actor modules covering suppliers, warehouses, work centers, labs, transport and quality management, each running as an independent Kafka consumer and synchronised by a cycle-clock protocol we wrote ourselves.
• Bayesian optimization for simulation calibration, using Gaussian process surrogates with Matérn kernels.
• MRP and finite-capacity job-shop scheduling, with custom heuristic solvers over bill-of-material and routing graphs.
• Agentic LLM orchestration with the Anthropic SDK, LangGraph and MCP, including a bridge that lets a server-side agent call tools that live in the browser. We build and use these ourselves, including Claude Code in production.
• AWS via Terraform.

Alongside the product, adago supports thesis-level research with a leading German university on self-supervised learning for supply chains: a heterogeneous graph encoder pretrained to predict masked latent states against a slowly updated target, trained on data generated by the same simulation engine you would be working on. You sit close to that work without being pulled out of the product.

• Junior0 to 2 years

  No prior professional experience needed. You pair with senior engineers and ship production work early.

• Mid2 to 5 years

  You have built production systems before and own model components end to end, across paradigms.

• Senior5+ years

  You have shipped simulation and optimization systems, set technical direction and mentor engineers.

These apply at every level. How much depth we expect scales with where you join, so read them against the levels above rather than as one fixed bar.

• A strong MSc in Business Mathematics, Statistics, Mathematics, Computer Science or a related quantitative field, with a PhD an advantage but never a requirement.
• Exceptional analytical rigour, and the ability to reason precisely through hard, ambiguous problems, with the depth we expect scaling with the level you join at.
• Production-grade Python, and comfort close to our agentic stack of Anthropic SDK, LangGraph and MCP: fluency at entry, ownership of the design at senior level.
• Foundations in probability, stochastic processes and optimization are essential at every level, with breadth growing from there. Bayesian methods are a strong plus.
• Any exposure to heavy-tailed distributions or extreme value methods is welcome, though it is a nice to have rather than something we expect you to arrive with.
• English is fully sufficient for this role and no German is required. The team works in English, and the position reports to the CTO rather than facing clients.

You report directly to the CTO. From here the typical path runs toward Senior AI Engineer and Tech Lead, with deeper technical ownership, more architectural decision-making and less day-to-day pairing.