• Act as the lead technical authority in high-stakes engagements • Partner with the commercial team to architect winning solutions • Lead the delivery of enterprise-grade systems such as GenAI agents, real-time recommendation engines, or predictive maintenance models • Own the complete technical lifecycle for projects, designing end-to-end ML architectures on GCP and implementing robust MLOps pipelines • Collaborate with the Head of Delivery to define the technical DNA of ML practice, evolving best practices • Spearhead the development of internal accelerators and reusable frameworks • Formally mentor and coach junior and mid-level engineers through code reviews and technical guidance

• 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.