Group AI/ML Solutions Architect - MySQL

Job Summary

Expectation for all engineer profiles

• Foundational AI/ML & Software Engineering
  • Strong grounding in ML fundamentals and software engineering, enabling translation of business problems into robust, scalable AI/ML solutions
  • Experience in designing, building and integrating production-grade systems using modern engineering practices (APIs, microservices, CI/CD, containerization)
  • Ability to bridge classical ML approaches with emerging GenAI paradigms, applying the right techniques to deliver reliable and maintainable solutions
  • Effectively leverage AI-assisted development tools (e.g., GitHub Copilot, Claude Code) to accelerate prototyping, improve engineering quality and enhance developer productivity
• Product Collaboration & Enablement
  • Ability to work effectively within agile product teams, collaborating in iterative cycles to refine requirements, validate hypotheses and deliver incremental AI/ML value
  • Ability to drive alignment independently across product, AI/ML engineering, platform and MLOps teams to achieve shared engineering outcomes
  • Strong capability in early-stage AI/ML solution development, including problem framing, feasibility assessment, rapid prototyping and iterative experimentation
  • Effective collaboration across geographically distributed teams (Denmark, India, Portugal), with strong cross-cultural awareness and communication

Key Responsibilities

Responsibilities:

• Lead design, development, delivery and scaling of ML, LLM and agentic solutions to deliver measurable business impact across Vestas value chain
• Build and contribute hands-on to robust ML pipelines, workflows and modeling efforts, ensuring reproducibility, strong CI/CD integration, data/feature consistency and high standards for code quality, testing and deployment
• Drive integration of AI/ML capabilities into enterprise applications, enabling seamless adoption and value realization
• Own and drive system architecture decisions to ensure scalable, reliable, cost-efficient and maintainable AI/ML solutions
• Establish and promote reusable frameworks, patterns and engineering standards to improve team productivity and solution scalability across teams

Skill Requirements

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, build and operate scalable batch and real-time ML pipelines for training & inference with strong reproducibility across environments
  • Build and orchestrate automated end-to-end ML workflows, integrating CI/CD practices and ensuring data & feature consistency across environments
  • Lead end-to-end productionization of AI/ML solutions, including enterprise integration via microservices, APIs and Docker based containerization
  • Apply advanced MLOps practices, including scalable system design, production-grade engineering, ML governance and robust monitoring
  • Manage full ML lifecycle, including versioning, governance, automated retraining and resilient deployment strategies
  • Demonstrate hands-on expertise in Git/Azure DevOps and modern build/test/deploy tools, with experience in enterprise ML platforms (e.g., Databricks MLflow, AI Foundry)

Other Requirements

• AI Engineer
• • Specialist: 8-12+ years of experience in software engineering, data or analytics, with significant hands-on experience and demonstrated impact in AI/ML solution development, including 4-6+ years focused on AI/ML

AI/ML Engineer with focus on solution design, engineering and scaling from prototype to reliable, integrated production systems - Specialist / Senior Engineer

End-to-End Ownership

Tell us about an AI/ML solution you took from idea to production. What were the key architecture decisions and what trade-offs did you make?

Scaling & System Design

Describe a case where your ML solution needed to scale beyond pilot. What changes did you make to support scaling? What trade-offs did you consider?

Failure & Learning Mindset

Describe a production ML system that failed or underperformed. What did you do differently afterward in your approach?

Engineering Standards & Reusability

Have you created reusable ML component, pipeline or standards across teams? What problem were you solving and what was adopted?

For one of the use cases above, share:
- One specific challenge in the solution flow and how you addressed it
- 2–3 concrete ways you validated that the solution worked as expected (metrics, checks or feedback loops)