Lead Data & AI Engineer - R&D IT

What You'll Do

• Stakeholder Collaboration: Work with project managers, resource managers, IT teams, and other stakeholders to gather requirements, define project scope, and ensure alignment with business objectives. • Generative AI Solutions: Design, develop, and deploy AI products to support R&D engineers. • Responsible AI and Reliable Data: Collaborate with data engineers and DevOps engineers to ensure high‑quality, secure, reliable, and well‑governed data availability for AI initiatives. • Automation & DevOps: Build AI solutions with a DevOps mindset—ensure reproducibility, automation, and quality by leveraging CI/CD pipelines, version control, and modern development practices. • Agile Delivery & Collaboration: Work as part of a Scrum team, operating within an Agile environment and participating in sprint planning, reviews, and cross‑functional collaboration to ensure rapid, iterative delivery of AI solutions.
• What you bring You can describe yourself as follows: Education & Experience: • Education: A master’s degree in Artificial Intelligence, Data Science, Software Engineering, Computer Science, or a related field.
• PhD in Artificial Intelligence, Machine Learning or related discipline is a plus. • Experience: 7+ years of experience in applied AI or AI solution development (professional or academic), preferably within a complex IT or R&D environment.
• Technical Skills • Coding: Strong proficiency in Python. • Generative AI Development: o Experience with foundational and open source model families (Claude, GPT, Llama, Mistral, Gemma, etc.). o Experience with RAG frameworks and components (LangChain, LlamaIndex, semantic search, embeddings, vector stores, etc.). o Familiarity with multimodal and agentic architectures. • Application Deployment: o Experience with Backend/API development (FastAPI, Flask, or equivalent). o Understanding of containerization and orchestration (Docker). • Automation: A strong interest in automation, cloud technologies, Infrastructure as Code (preferably using CDK), DevOps practices, and CI/CD pipelines (preferably using Gitlab). • Cloud: Experience working with cloud-native AI deployment on AWS (or other cloud platform) is a plus.
• Professional Attributes • Strategic Problem-Solving: Comfortable owning technical challenges and designing long-term, scalable solutions. • Customer & Stakeholder Focus: Strong communicator who can translate technical concepts into business value and collaborate effectively across data science, architecture, and wider R&D. • Team Mindset: A natural collaborator who contributes to an open, supportive working culture • Agile & SCRUM: Experienced working in Agile environments, actively participating in sprints, stand-ups, and iterative delivery cycles to ensure continuous improvement and timely value delivery.
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