• Develop and support all Machine Learning and data curation activities; • Develop and integrate autonomous agents based on LLMs for complex tasks; • Implement RAG pipelines (indexing, semantic search, integration with vector databases); • Create scalable architectures for orchestrating multiple agents (LangChain, LangGraph); • Fine-tune and optimize LLMs for specific contexts; • Ensure security, governance and compliance in AI solutions (LGPD, AI Act); • Define metrics and continuously validate the quality of responses (accuracy, relevance, consistency); • Monitor performance and apply techniques to reduce cost and latency (quantization, distillation); • Lead and support all Machine Learning and data curation activities; • Develop and integrate autonomous agents based on LLMs for complex tasks; • Implement RAG pipelines (indexing, semantic search, integration with vector databases); • Create scalable architectures for orchestrating multiple agents (LangChain, LangGraph); • Fine-tune and optimize LLMs for specific contexts. Ensure security, governance and compliance in AI solutions (LGPD, AI Act); • Define metrics and continuously validate the quality of responses (accuracy, relevance, consistency); Monitor performance and apply techniques to reduce cost and latency (quantization, distillation).

• Drive alignment across teams on ML strategy, standards, and long-term technical direction by serving as a technical leader for Lime’s ML Center of Excellence • Guide recommendations for ML infrastructure, tooling, and architecture (training, serving, feature stores, experimentation, monitoring) • Define and evolve ML development processes, including model review, experimentation rigor, deployment, optimization, and operations • Establish best practices for ML monitoring, observability, alerting, and model performance health in production • Drive reusable feature development patterns and shared ML capabilities that enable teams to move faster and more safely • Partner with platform, data, and product engineering teams to ensure ML systems are reliable, scalable, and cost effective • Identify and prioritize opportunities where ML will improve Lime’s product, operations, or efficiency • Act as a force multiplier by mentoring data scientists and machine learning engineers, raising the quality bar for machine learning across Lime

• Design, train, test, and iterate on diffusion models for 3D geological models • Design, train, test, and iterate on an approach to for conditioning generation on geophysical data and other observations • Inform the generation of synthetic data to improve model performance • Adapt diffusion modeling approach to specific real-world projects in collaboration with project teams.

• Design and maintain training systems that can process and learn from petabyte-scale multimodal datasets (e.g., video and point cloud data). This includes ensuring data is efficiently loaded, distributed, and processed across large GPU clusters. • Identify and resolve bottlenecks in the training pipeline, including data loading, preprocessing, model computation, and inter-node communication, to maximize GPU utilization and reduce training time. • Work with the ML team to develop and refine neural network architectures suitable for autonomy tasks, particularly those handling high-dimensional and sequential sensor data. • Create and adjust loss functions and training strategies that help the model learn effectively from complex multimodal inputs and improve autonomy performance. • Configure, monitor, and maintain large-scale distributed training jobs across multiple machines and GPUs, ensuring stability, fault tolerance, and efficient resource usage. • Implement scalable systems to preprocess, transform, and augment large robotics datasets so that they are suitable for model training. • Work closely with ML scientists and other engineers to integrate new models, experiments, and training approaches into the production training pipeline. • Analyze training metrics, model outputs, and experiment logs to assess model performance and guide improvements in architecture, data usage, or training strategies. • Develop tools and workflows that allow teams to run experiments, track results, and iterate quickly on new model ideas or training approaches.