Teams at EcoVadis

Build and operate production-grade knowledge graph infrastructure, GraphRAG pipelines, entity-resolution workflows, semantic data integrations, and agent tooling. The role involves transforming structured and unstructured sustainability data into graph databases, optimizing multi-hop queries, implementing hybrid vector-graph search, exposing graph tools to LLM agents, and enforcing data quality with SHACL validation. Candidates will use Python, cloud technologies, NLP frameworks, semantic web standards, and graph databases at enterprise scale.

Design and deploy multi-turn conversational AI systems, agentic workflows, and LLM-powered solutions for complex enterprise domains. Lead AI initiatives, mentor junior staff, promote engineering best practices, and collaborate with customers, engineers, and product managers. Build, benchmark, and maintain end-to-end models using Python, Azure, MLflow, and Databricks while evaluating emerging generative AI technologies.

Design, deploy, and maintain scalable AI/ML systems and infrastructure. Build MLOps, LLMOps, and AgentOps pipelines; orchestrate batch and real-time workflows; run experiments; monitor models; and integrate AI solutions into business applications. The role uses Python, MLflow, Azure services, and Databricks, while collaborating with scientists, engineers, and product teams to deliver production-grade AI/ML products.