ML Engineer

ML Engineer, Applied AI role focused on deploying and maintaining production ML systems (classical NLP, embeddings, and LLM-powered features) at scale, owning evaluation stacks (golden datasets, inter-annotator agreement, regression tests), and building a vector embeddings ecosystem with retrieval and similarity pipelines. Collaborates with Data Science and Engineering on annotation workflows, PII scrubbing, and ground-truth pipelines; improves MLOps foundations (versioning, observability, drift detection) and translates product problems into measurable AI features with clear success criteria. Requires 4–7 years of professional software or ML engineering experience (2+ years shipping ML systems to production), strong Python, cloud experience (AWS or GCP), and production NLP/ML experience (classification, NER, embeddings, ranking, similarity, or LLM features); excellent collaboration and communication with technical and non-technical stakeholders. Bonus: vector databases, managed ML services (SageMaker, Vertex AI), annotation tooling, privacy-by-design data handling, and open-source contributions. This role supports a flexible, hybrid work model with in-person presence in Warsaw, Poland.