This list contains only the countries for which job offers have been published in the selected language (e.g., in the French version, only job offers written in French are displayed, and in the English version, only those in English).
This is the senior-level version of an AI Engineer: same end‑to‑end ownership, but with more leadership, more influence, and bigger, messier, more rewarding problems. This team isn’t building novelty demos. They’re deploying AI into live products across multiple regions. Your work will touch real customers, improve real operations, and directly influence commercial performance.
Job Responsibility:
Take full technical ownership of AI systems – from prototype to production reliability
Make architectural calls that balance speed, stability, and long-term maintainability
Convert loosely defined business questions into clear AI solutions with measurable impact
Build everything from classical ML models to LLM-powered services (RAG, fine-tuning, adapters, you name it)
Deeply integrate your models into apps, CRM platforms, workflows, digital journeys, and internal tools
Lead experiments, run A/B tests, validate hypotheses, and guide stakeholders through trade‑offs
Set up monitoring, drift checks, retraining flows, observability, and proper MLOps hygiene
Work hands-on with data, modelling, pipelines, APIs, orchestration – the full engineering spectrum
Mentor juniors, shape engineering standards, and help create reusable blueprints for future AI builds
Act as a champion for safe, transparent, responsible AI use
Requirements:
8+ years working in ML, data science, or software engineering with real AI ownership
Has shipped multiple AI/ML systems into production
Has led at least one meaningful end‑to‑end AI initiative (technical direction + stakeholder alignment)
Comfortable with Python + SQL and enjoys writing clean, production-level code
Knows ML frameworks (scikit‑learn, XGBoost, PyTorch, TensorFlow, LightGBM etc.)
Has experience with LLMs and generative AI (APIs, RAG, evaluation, tuning)
Understands data engineering basics and can work with pipelines, warehouses, and orchestration tools
Familiar with the cloud (Azure, AWS, or GCP all welcome)
Navigates ambiguity well – shaping scope instead of waiting for it
Communicates clearly, especially with non‑technical stakeholders
Loves mentoring, pair programming, reviewing code, and raising team standards
Understands privacy, safety, and the need for responsible AI