• Contribute to multiple geospatial AI and Earth observation initiatives. • Focus on operationalisation of GeoAI services using AI and large-scale Earth observation processing. • Support crop type mapping projects and scale algorithms for digital agriculture. • Develop and improve ML/CV algorithms for large-scale remote sensing data processing on Sentinel data. • Analyse data flows and optimise databases and queries. • Collaborate with technical and non-technical stakeholders in an international setting.

• Design, build, and deploy AI-enabled services for the Compendia Data Platform (CDP), including LLM-based NLP capabilities and interoperable (agentic) components. • Develop and maintain evaluation frameworks for LLM - ontology/knowledge base integration, including quantitative metrics, baselines, error analysis, and reporting to support defensible architecture decisions. • Support ontology lifecycle management (modeling, inference, validation, and lifecycle workflows) as the ontology module matures from MVP to GA. • Partner with product and engineering to improve platform stability, performance, and scalability for CDP v1.8 and beyond. • Contribute to proofs of concept (PoCs), demos, and mission-aligned analytics workflows while sustaining LANL delivery commitments. • Stay current on relevant research and emerging patterns for semantic engineering, LLM evaluation, and production AI systems; translate findings into implementable, testable solutions. • Contribute to a high-performing team culture by delivering mission-aligned analytics and software tools that ensure high customer satisfaction and meet strict delivery commitments.

• Extract, transform, aggregate, and analyze large and complex datasets using SQL, Python, R, Spark, and related technologies. • Perform exploratory data analysis (EDA), feature engineering, and data validation to support analytics and modeling initiatives. • Develop, evaluate, and maintain descriptive and predictive machine learning models using tools such as scikit-learn, Jupyter Notebooks, Python, R, and/or SAS. • Apply statistical modeling and data mining techniques, including regression, classification, clustering, decision trees, and related methodologies. • Utilize Generative AI and AI-assisted tools, including LLMs, coding assistants, and AutoML platforms, to improve analytical workflows and insight generation. • Apply Generative AI techniques such as prompt engineering, text summarization, classification, and LLM-assisted analysis in business or research applications. • Collaborate with stakeholders to translate business problems into analytical solutions and communicate findings effectively. • Build and maintain business intelligence solutions and dashboards using Tableau, Power BI, or similar visualization platforms. • Define metrics, validate data quality, and support semantic layer development to ensure accurate reporting and business insights. • Follow responsible AI principles, including awareness of data privacy, bias mitigation, ethical AI usage, and model limitations.

• Extract, transform, aggregate, and analyze large and complex datasets using SQL, Python, R, Spark, and related technologies. • Perform exploratory data analysis (EDA), feature engineering, and data validation to support analytics and modeling initiatives. • Develop, evaluate, and maintain descriptive and predictive machine learning models using tools such as scikit-learn, Jupyter Notebooks, Python, R, and/or SAS. • Apply statistical modeling and data mining techniques, including regression, classification, clustering, decision trees, and related methodologies. • Utilize Generative AI and AI-assisted tools, including LLMs, coding assistants, and AutoML platforms, to improve analytical workflows and insight generation. • Collaborate with stakeholders to translate business problems into analytical solutions and communicate findings effectively. • Build and maintain business intelligence solutions and dashboards using Tableau, Power BI, or similar visualization platforms. • Follow responsible AI principles, including awareness of data privacy, bias mitigation, ethical AI usage, and model limitations.