Machine Learning Engineer

figure out what's possible and make it real. We are looking for a Machine Learning Engineer who wants to define what comes next. What you will do: - Design, build, and deploy AI-powered features for Bynder's DAM platform, focusing on content discoverability, automation, and intelligent workflows. - Architect and implement solutions using AWS AI services (Bedrock, SageMaker, Lambda) and large language model APIs. - Build and optimize embedding-based systems for AI search, semantic retrieval, and content recommendation. - Develop agentic AI workflows that automate complex tasks such as asset enrichment, tagging, transformation, and compliance checking. - Create production-quality prototypes and proof-of-concepts that demonstrate the value of new AI capabilities to stakeholders. - Stay current with the rapidly evolving AI landscape and bring fresh ideas and techniques to the team. - Collaborate with the broader engineering organization to ensure smooth handoff of prototypes into

production systems. - Collaborate directly with product stakeholders and end-users to validate AI prototypes and gather feedback for successful delivery What you will bring as a Machine Learning Engineer: - Minimum of 3+ years of experience in Machine Learning Engineering, AI Engineering, Data Science, or a similar role. - Strong understanding of how large language models work: transformer architectures, attention mechanisms, tokenization, embeddings, and inference pipelines. - Hands-on experience with cloud AI services, particularly AWS (Bedrock, SageMaker, Lambda). - Practical experience building systems with embeddings and vector databases for semantic search or retrieval-augmented generation (RAG). - Familiarity with agentic AI frameworks (e.g., LangChain, LangGraph, CrewAI, or similar) and understanding of agent architectures including planning, memory, and tool use. - Strong software engineering fundamentals: Python, REST APIs, Git, Docker, and CI/CD.

• Solid foundation in mathematics: linear algebra, probability, statistics, and optimization. Understanding of classical ML algorithms and when they apply. - Bachelor's degree in Computer Science, AI, Mathematics, Physics, or a related field. Bonuses: - Master's degree or PhD in a relevant field. - Experience with prompt engineering, context engineering, and LLM evaluation frameworks. - Knowledge of MLOps practices: model monitoring, A/B testing, and deployment pipelines for AI features. - Experience with computer vision or image/video processing (highly relevant to our DAM domain). - Proficiency with AI-assisted coding tools and workflows (GitHub Copilot, Cursor, Claude Code, etc.). - Familiarity with GDPR and data privacy considerations for AI systems in Europe. - Experience working in a B2B SaaS or enterprise software environment. What you will bring as a colleague: - A self-starter mentality. You thrive in an R&D environment with ambiguity and open-ended challenges. - Intellectual curiosity and a

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