Machine Learning Engineer
Required skills
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.
Preferred skills
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.
Technologies
AWS AI services (Bedrock, SageMaker, Lambda), large language model APIs, embeddings, vector databases, agentic AI frameworks (LangChain, LangGraph, CrewAI), Python, REST APIs, Git, Docker, CI/CD, MLOps practices, computer vision or image/video processing, AI-assisted coding tools, GDPR and data privacy considerations.
Responsibilities
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.
Seniority
Not explicitly stated.
Domain
Digital Asset Management (DAM), AI, Machine Learning, Cloud Computing, Software Engineering, MLOps, Computer Vision, Data Privacy.
