ai / ml engineer (sme).

Mission 

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.

Key Responsibilities

Model Engineering (Core)

• 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
   

Enterprise-Grade AI Delivery

• 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)
   

Technical Leadership

• 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

Technical Scope

AI / ML Stack

• Classical ML, deep learning, representation learning

• LLMs: fine-tuning, adapters, embeddings, retrieval-augmented generation

• Model evaluation, explainability, bias mitigation, robustness testing

Tooling & Engineering

• Python as primary language

• ML frameworks: PyTorch, TensorFlow or equivalent

• Experiment tracking, model versioning, reproducibility

• Interaction with feature stores, inference services, and data platforms

Production Awareness

• Inference optimization (latency, throughput, cost)

• Model lifecycle management in production environments

• Close interaction with MLOps and infrastructure layers

Profile

Experience

• Strong background in applied AI or machine learning engineering

• Demonstrated experience delivering models into production

• Familiarity with enterprise-scale systems and constraints
   

Mindset

• Engineering-first approach to AI

• Comfortable operating in complex, ambiguous environments

• High standards for technical quality, reliability, and impact

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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.