• Implement and integrate agent-based systems into operational workflows. • Build, deploy, and monitor ML/AI models in production (batch). • Design, build, and maintain large-scale geospatial data pipelines. • Develop backend services and ML tooling • Establish observability for pipelines, models, and agents (metrics, tracing, alerting). • Collaborate with product and customer teams to drive revenue.

• Design and implement deep learning models for 3D computer vision tasks, including object detection, segmentation, and depth estimation. • Develop and maintain end-to-end machine learning pipelines encompassing data preprocessing, model training, evaluation, and deployment. • Optimize models for real-time inference and deploy them using cloud platforms such as AWS SageMaker or GCP Vertex AI. • Monitor deployed models, analyze performance metrics, and implement retraining strategies to ensure sustained accuracy and reliability. • Document methodologies, experiments, and findings; actively participate in code reviews and technical discussions. • Stay abreast of the latest research and advancements in machine learning and computer vision to inform model development.

• Develop scalable, production-ready LLM applications using frameworks like LangChain/LangGraph • Build robust RAG pipelines and integrate knowledge graphs for biological and clinical data • Write maintainable, high-performance code and build clean APIs and services for machine learning applications • Work with data engineers to build and optimize data workflows and pipelines for high-quality data ingestion and processing • Collaborate with product and domain teams to rapidly prototype AI solutions, iterate based on feedback, and scale models for production • Use modern MLOps tools to deploy and monitor models in production environments (AWS preferred) • Partner with engineering, data, and business teams to identify and develop high-value AI/ML applications • Stay ahead of the curve on emerging ML frameworks, GenAI capabilities, and healthcare technologies

• Spearhead the design, development, and deployment of ML/DL models into production. • Own the end-to-end lifecycle of machine and deep learning systems, from model deployment and monitoring, to retraining, governance, and reliability in production. • Define the standards, tooling, and architectural patterns that allow data scientists and analysts to safely and efficiently ship models that directly power our credit and business decisions.