Building Enterprise-Scale LLM Applications: A Practical Guide
Learn how to design, implement, and deploy large language models at enterprise scale with considerations for security, cost, and performance.
I am passionate about the opportunities created by modern data platforms and AI/ML. Leveraging over two decades of experience, including transformative work with MSCI, SLB/OSDU, and UnitedHealthcare, I help organizations design and implement scalable, resilient data platforms and intelligent systems that drive innovation, growth, and efficiency.
With deep expertise in cloud-native architecture, AI-driven solutions, and data engineering, I focus on aligning data strategies with business goals, fostering innovation through AI/ML solutions, and transforming data practices to support seamless scalability and robust data governance.
Specialized expertise to drive data and AI transformation across your organization
Designing scalable, cloud-native data platforms utilizing Lakehouse and Data Mesh architectures. Build robust data pipelines and implement modern data engineering practices.
Implementing advanced AI and ML solutions, including custom LLMs and intelligent automation. Create end-to-end ML pipelines and deploy models that deliver real business value.
Establishing robust data governance frameworks to ensure compliance, data quality, and security. Implement data catalogs, metadata management, and data quality monitoring.
Case studies showcasing real business impact
Collaborated with senior management to define and implement a comprehensive and data strategy, driving innovation and adoption of advanced technologies. Architected a NextGen Data Platform leveraging Lakehouse and Data Mesh architectures, optimizing scalability, security, and performance for the organization’s data needs.
Led the development of a reference architecture for an open data ecosystem in collaboration with energy industry leaders. This platform has revolutionized data accessibility and collaboration, setting a new standard for multi-cloud ecosystems in the sector.
Enhanced data collection and risk analysis through AI-driven solutions, increasing accuracy and operational efficiency. This AI integration empowered data-driven insights, setting a new standard for data intelligence and strategic decision-making.
Designed and implemented a multi-cloud CI/CD pipeline and Service Provider Interfaces (SPIs), improving operational efficiency across global teams and multiple cloud providers in a large-scale energy company.
Expert solutions to elevate your data strategy and drive digital transformation
Design and implementation of modern data platforms using cloud-native architectures. Specializing in Lakehouse architecture, Data Mesh patterns, and scalable data solutions that drive business value.
End-to-end ML/AI implementation from strategy to production. Custom LLM development, MLOps automation, and intelligent systems that transform business processes.
Establish robust data governance frameworks, implement data quality monitoring, and ensure compliance with industry regulations while maintaining data security.
Strategic cloud migration planning and execution. Optimize cloud-native architectures for cost, performance, and scalability across multiple cloud providers.
Transform raw data into actionable insights through advanced analytics and intuitive visualizations. Create automated reporting and real-time dashboards.
Align technology initiatives with business objectives through comprehensive enterprise architecture planning and implementation strategies.
Let's explore how we can unlock your data's potential.
Explore articles on data architecture, AI, and strategies for digital transformation.
Learn how to design, implement, and deploy large language models at enterprise scale with considerations for security, cost, and performance.
Explore real-world implementation strategies for data mesh architecture, including organizational changes, technical requirements, and success metrics.
A comprehensive guide to implementing MLOps in your organization, covering the entire machine learning lifecycle from development to deployment and monitoring.