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Our enterprise clients are moving from isolated AI experiments to large-scale, business-critical AI systems. AI is no longer a research topic. It is reshaping core processes, products, and operating models across industries. This role exists to support that shift by delivering enterprise-grade AI systems that perform reliably at scale. You will operate as a Subject Matter Expert in complex enterprise environments where performance, robustness, explainability, and scalability matter as much as model accuracy. Acting either in a client-based consultative model, outsourced delivery, or product-based engagement, you will own the end-to-end lifecycle of AI/ML models, from problem framing to production readiness, while serving as a technical authority on model engineering. This is not a research-only role and not a generic data science position. This role is designed to solve enterprise-grade problems using cutting-edge AI technologies, operating as a technical force multiplier across projects and client environments. You will position yourself as a Subject Matter Expert capable of pushing AI forward by multiple orders of magnitude, contributing to the construction of one of the most performant AI engineering teams across Europe.
Job Responsibility:
Design, train, fine-tune, and evaluate machine learning and deep learning models
Work with open-source frontier models and proprietary models depending on use case constraints (cost, latency, governance, IP)
Adapt architectures for real-world constraints: inference speed, memory, cost, reliability
Implement evaluation frameworks covering accuracy, robustness, bias, and explainability
Translate business problems into production-ready AI solutions
Collaborate closely with Data Engineers, MLOps, Infra, and Product teams
Design retraining, monitoring, and model lifecycle strategies (drift, degradation, rollback)
Act as AI/ML SME within multidisciplinary teams
Contribute to architectural decisions and technical standards
Support solution architects and product leads on AI feasibility and trade-offs
Review and challenge AI designs to meet enterprise-grade requirements
Requirements:
Strong background in applied AI or machine learning engineering
Demonstrated experience delivering models into production
Familiarity with enterprise-scale systems and constraints
Engineering-first approach to AI
Comfortable operating in complex, ambiguous environments
High standards for technical quality, reliability, and impact