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Own a strategic sustainability product use case end to end, from customer discovery and data requirements through AI-enabled prototyping, roadmap, engineering delivery, launch, go-to-market, and measurable business outcomes. Engage enterprise customers, define product vision, assess competitors, collaborate across squads, and make persist, pivot, or stop decisions. The role requires strong technical fluency, AI-native workflows, customer obsession, and experience delivering B2B enterprise SaaS products in regulated, data-rich, sustainability, or risk domains.

Own a strategic sustainability product use case end to end, from customer discovery and data requirements through AI prototyping, roadmap development, engineering execution, launch, and market outcomes. Engage enterprise customers, assess competitors, define success measures, partner with commercial teams, and make persist, pivot, or stop decisions. The role requires an AI-native, technically fluent product builder with experience in regulated, data-rich, sustainability, or risk domains.

Own a strategic sustainability product use case end-to-end, from customer discovery and data requirements through AI-powered prototyping, roadmap, development, launch, and market outcomes. Engage enterprise customers, analyze competitors, collaborate with engineering and commercial teams, define success metrics, and make persist, pivot, or stop decisions. The role requires an AI-native, technically fluent product builder comfortable with regulated, data-rich sustainability or risk domains.

Own a strategic sustainability product use case end to end, from customer discovery and data requirements through AI-enabled prototyping, roadmap, development, launch, and market outcomes. Partner with customers, engineering, product experts, and commercial teams to define vision, validate solutions, drive execution, and measure results. The role requires an AI-native, technically fluent product builder with enterprise SaaS experience and comfort working in regulated, data-rich, sustainability, or risk domains.

Own a strategic sustainability product use case end-to-end, from customer problem discovery and data requirements through prototyping, roadmap, launch, go-to-market, and measurable outcomes. Engage enterprise customers, collaborate with engineering and commercial teams, use AI and scripting to prototype solutions, define success metrics, and make product decisions. The role requires an AI-native, customer-focused generalist comfortable working in regulated, data-rich sustainability or risk domains.

Leads a large engineering team of approximately 20 people responsible for a core B2B SaaS product domain. Partners with Product and Design leadership on product vision, roadmap, architecture, delivery, quality, staffing, and technical strategy. Provides technical direction, manages technical debt, improves operational excellence, coordinates cross-functional priorities, and mentors senior talent in an international environment.

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.

Design, deploy, and maintain scalable AI/ML systems and MLOps, LLMOps, and AgentOps pipelines. Build batch and real-time infrastructure, conduct large-scale testing, monitor models, and integrate AI solutions into business applications. Collaborate with scientists, engineers, and product teams using Python, Azure, MLflow, and Databricks.