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Role Overview RMSI is looking for an experienced AI/ML professional to lead the development of scalable AI solutions across sustainability, geospatial intelligence, climate risk, Earth observation, infrastructure, and enterprise analytics. The role combines hands-on technical leadership with AI strategy, team development, productization, and client engagement. Key Responsibilities Define and execute the AI/ML strategy, technology roadmap, architecture, and governance framework. Lead multidisciplinary teams of AI/ML engineers, data scientists, geospatial specialists, data engineers, and MLOps professionals. Develop AI solutions using geospatial data, satellite imagery, weather and climate datasets, scientific models, and enterprise data. Drive the adoption of Generative AI, computer vision, multimodal AI, RAG, knowledge graphs, time-series models, and AI agents. Establish MLOps and LLMOps pipelines for model deployment, evaluation, monitoring, retraining, version control, and cost optimization. Convert research and prototypes into secure, explainable, scalable, and commercially viable AI products. Establish responsible AI standards covering model validation, explainability, bias, data privacy, security, and human oversight. Support solution design, client engagements, proposals, partnerships, demonstrations, and AI-led business growth. Mentor technical teams and conduct architecture, model, and code reviews. Required Profile Masters degree or PhD in AI, Computer Science, Data Science, Geoinformatics, Engineering, or a related discipline. 810 years of overall experience, including at least 46 years of hands-on AI/ML experience and demonstrated technical leadership. Strong knowledge of machine learning, deep learning, computer vision, NLP, Generative AI, and time-series modelling. Advanced proficiency in Python and frameworks such as PyTorch, TensorFlow, scikit-learn, and Hugging Face. Experience with LLMs, prompt engineering, embeddings, vector databases, RAG, and AI-agent frameworks. Experience with cloud platforms, data engineering, APIs, microservices, Docker, Kubernetes, and MLOps. Preferred Experience GeoAI, GIS, satellite imagery, remote sensing, and spatial analytics. Sustainability, climate risk, natural resources, infrastructure, utilities, or telecommunications. Scientific machine learning, graph learning, digital twins, or high-performance computing. .