Senior ML Engineer

Responsibilities As a senior member of the product team, you will partner with product owners, ML engineers, application developers and business SMEs to build and scale AI/ML capabilities within digital products. This role carries significant informal leadership, with a clear focus on accelerating delivery and improving AI/ML impact across the organization Manage the design, delivery and scaling of ML, LLM and agentic solutions to deliver measurable business impact across Vestas value chain Embed AI/ML capabilities into enterprise applications, enabling seamless adoption and value realization Own and make system architecture decisions to ensure scalable, reliable and maintainable AI/ML solutions Establish and promote reusable frameworks, patterns and engineering standards across teams Own end-to-end delivery across multiple initiatives, balancing speed, quality and evolving priorities Shape stakeholders on AI/ML strategy, guiding solution design aligned with business goals and governance standards Qualifications AI/ML Solutions Experience Bachelor's or Master's degree in Computer Science / Engineering / Data Science / or similar specialization 10+ years of experience in software engineering, data or analytics, focusing on digital solutions development 4-6 years in ML engineering, specializing in designing, building and productionizing ML solutions with continuous delivery and deployment Solid practical experience in ML modelling, experiment tracking and building scalable training and inference pipelines using cloud-based CI/CD tools Extensive experience overseeing the full production rollout of AI/ML solutions, including enterprise integration using microservices, APIs, and Docker containers Working knowledge of MLOps practices, including scalable system design, ML governance, production-grade engineering, and robust monitoring (system health, data validation, and drift detection) Competencies Scalable AI/ML Systems & Deployment Engineering Productionize ML, LLM/GenAI models and agentic systems into reliable, high-performance services with optimized latency, throughput and cost efficiency Design and operate scalable batch and real-time ML pipelines with high reproducibility across training and deployment Build and orchestrate automated end-to-end ML workflows, integrating CI/CD practices and ensuring data/feature consistency Establish robust monitoring and observability for model performance, data quality, system health and drift detection Manage the full ML lifecycle with solid versioning, governance, automated retraining and resilient deployment strategies Demonstrate solid knowledge of Git/Azure DevOps and modern build/test/deploy tools, with experience in enterprise ML platforms (e.g., Databricks MLflow, AI Foundry) Effectively leverage AI-assisted development tools (e.g., GitHub Copilot, Claude Code) to accelerate prototyping, improve engineering quality and enhance developer productivity in building AI/ML and GenAI solutions Global Collaboration & Enablement Effective collaboration across geographically distributed teams (Denmark, India, Portugal), with effective cross-cultural awareness and communication Ability to create alignment across product, AI/ML engineering, platform and MLOps teams to support shared engineering outcomes Demonstrated capability in mentoring and enabling teams through knowledge sharing, best practices and informal leadership