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:

• Design, build and scale GenAI and agentic systems capable of reasoning, planning, and autonomously executing complex tasks
• Lead the design and delivery of end-to-end solutions leveraging LLMs, multi-agent workflows, memory and tool integration frameworks
• Develop intelligent workflows using techniques such as prompt engineering, RAG, context orchestration and function/tool calling
• Integrate GenAI capabilities into enterprise applications, enabling seamless, human-in-the-loop and autonomous decision-making
• Define and promote reusable design patterns, frameworks and engineering standards for agent-based and generative AI systems
• Own end-to-end delivery while advising stakeholders on feasibility, balancing experimentation with production considerations such as performance, cost, reliability and governance

Skill Requirements

Competencies:

• LLM Systems Architecture & Delivery

  • Architect scalable, reliable GenAI systems with focus on performance, cost, latency and user experience
  • Establish reusable design patterns and frameworks for building and scaling agentic and LLM-based solutions
  • Drive end-to-end delivery of GenAI systems from prototyping to production adoption

• GenAI & Agentic Systems Design

  • Design and build generative AI and agent-based that support reasoning, planning and semi-autonomous execution of complex workflows
  • Apply advanced techniques including RAG, prompt orchestration, memory management and tool/agent coordination
  • Translate business problems into intelligent AI workflows combining human-in-the-loop and autonomous decision-making
  • Apply responsible AI practices, including guardrails, bias mitigation, and safe system behavior
  • Ability to implement end-to-end ML workflows, including data preprocessing, model development, evaluation and deployment, with support for HITL and decision-support systems
  • Good to have: experience with document intelligence and NLP/LLM use cases such as contract analytics, automated information extraction, intelligent document processing, semantic search and AI-driven automation workflows

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 GenAI/Agentic solutions (LLMs, agents, workflow automation) - Specialist

End-to-End GenAI System

Tell us about a recent NLP, document intelligence, or GenAI solution you designed and took to production. What was the architecture, major design decisions and key challenges during productionization?

LLM Reliability & Evaluation

Describe how you evaluated and improved the quality of an NLP or LLM-based system in production. Cover observability and how you safeguarded against regressions

Production Trade-offs (Latency / Cost / UX)

Share a case where your NLP/GenAI solution had performance, cost, or accuracy trade-offs in production.

Frameworks & Reusability

Have you built reusable patterns or frameworks for GenAI/agent systems across teams? What scaled and what didn’t?

For one of the use cases above, share:
 - One key challenge in the solution flow and what approach you took to resolve it
 - 2–3 specific metrics, checks or feedback mechanisms you used to validate correctness and quality