Senior ML Engineer (NLP/GenAI)

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 advanced GenAI and agent-based systems embedded in digital products. This role emphasizes reasoning-driven AI, self-directed execution and significant informal leadership to support broader organizational goals. Design, build and scale GenAI and agentic systems capable of reasoning, planning, and effectively executing complex tasks Build 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 professional decision-making Define and promote reusable design patterns, frameworks, and engineering standards for agent-based and generative AI systems Own end-to-end delivery of GenAI initiatives while advising stakeholders on use case feasibility and responsible AI adoption balancing experimentation with production considerations such as performance, cost, reliability, and governance Qualifications Bachelor's / Master's degree in Computer Science / Engineering / Data Science / Similar specialization with 10+ years of experience in software engineering, data or analytics, focusing on digital solutions development 4-6 years of experience in AI/ML solution development, ML engineering, with a focus on NLP and applied GenAI systems Practical experience with LLM ecosystems (e.g., OpenAI, Azure OpenAI, open-source models), prompt engineering and context design Practical experience with RAG architectures, embeddings, vector databases and knowledge-grounded AI systems Experience integrating GenAI solutions into enterprise applications using APIs, microservices, and containerized environments Familiarity with evaluation techniques for LLMs (factuality, hallucination mitigation, safety and quality benchmarking) Understanding of LLMOps practices, including versioning, monitoring, cost control, and governance Experience/exposure to designing agentic workflows, including planning, tool usage, memory and multi-step reasoning pipelines 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 Manage end-to-end delivery of GenAI systems from prototyping to production adoption GenAI & Agentic Systems Design Design and build generative AI and agent-based systems that support reasoning, planning, and self-directed 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 self-directed decision-making Apply responsible AI practices, including guardrails, bias mitigation, and safe system behavior Foundational AI/ML & Software Engineering Solid 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 in building AI/ML and GenAI solutions Global Collaboration & Enablement Effective collaboration across geographically distributed teams (Denmark, India, Portugal), with cross-cultural awareness and communication Ability to build alignment across product, AI/ML engineering, platform and MLOps teams to shared engineering outcomes Demonstrated capability in mentoring and enabling teams through knowledge sharing, best practices and informal leadership