Senior AI/ML Engineer

Role overview

A senior AI/ML Engineer position focused on the end-to-end delivery of AI and Generative AI solutions, spanning ideation, experimentation, production deployment, and continuous improvement. The role combines hands-on machine learning engineering with architecture, technical strategy, AI governance, and technical leadership. Solutions delivered in this capacity support domains such as dealer operations, back-office processes, document processing, decision-support systems, and intelligent data platforms.

Responsibilities

- Lead the design and delivery of complex machine learning and AI solutions aligned with business objectives - Define ML architecture standards, development patterns, and engineering best practices across AI initiatives - Drive technical strategy and technology selection, and oversee end-to-end ML lifecycles covering data preparation, feature engineering, training, evaluation, deployment, monitoring, and retraining - Build and deploy production-grade ML models using modern frameworks and cloud-based platforms - Collaborate with data engineers, MLOps engineers, solution architects, and business stakeholders to ensure scalable, production-ready outcomes - Establish industrialization practices including CI/CD, observability, monitoring, model lifecycle management, and operational excellence - Provide technical leadership across delivery teams and mentor other AI/ML engineers - Apply AI governance and responsible AI practices, including compliance activities around risk classification, transparency mechanisms, technical documentation, event logging, and audit readiness - Support production troubleshooting and ensure long-term maintainability of AI solutions - Contribute to knowledge sharing and capability building within internal AI and automation teams

Requirements

- 6+ years of professional experience in machine learning or AI engineering - Strong Python programming skills with hands-on experience using PyTorch, TensorFlow, or scikit-learn - Proven track record of building and deploying ML models in production environments - Solid understanding of model evaluation, experimentation, performance monitoring, and ML lifecycle management - Experience with MLflow or similar ML lifecycle management platforms - Hands-on experience with cloud-based ML platforms, preferably Azure ML; Vertex AI or AWS SageMaker also relevant - Strong grasp of MLOps and CI/CD practices - Experience working with both structured and unstructured data - Understanding of AI governance, responsible AI principles, model documentation, and compliance requirements - Experience collaborating with cross-functional Agile teams and engaging directly with business stakeholders - Strong analytical and problem-solving capabilities - Excellent communication and stakeholder management skills - Experience providing technical guidance or mentoring to other engineers

Nice to have

- Experience with Generative AI solutions and LLM-based applications - Familiarity with vector databases and embedding models - Knowledge of Retrieval-Augmented Generation (RAG) architectures - Experience with document intelligence and OCR solutions - Knowledge of Azure OpenAI services - Background in automotive, mobility, retail, or dealer-network environments - Familiarity with blue/green, canary, rolling, or shadow deployment strategies - Experience supporting AI systems in regulated environments

Benefits and work setup

- Remote work from within the European Union region is required, along with a valid work permit