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Builds and leads the data infrastructure for Google Cloud’s AI agents, designing scalable pipelines, semantic models, and real-time systems to power secure, high-performance generative AI applications.
Build scalable data pipelines and AI/ML models for anti-financial crime at a UK bank, using Python, LLM orchestration, and distributed frameworks like PySpark and GCP.
Design and maintain enterprise-scale AI platforms, deploying and optimizing LLMs and vision models on NVIDIA SuperPods/Cloud using Kubernetes, Docker, and CI/CD pipelines.
Design and run enterprise-scale AI platforms, deploying LLMs and vision models on NVIDIA GPU clusters, optimizing inference pipelines with Kubernetes and OpenShift, and ensuring high availability and performance.
Build and deploy AI/ML models for Saudi Aramco’s energy and sustainability projects using Python, LLMs, and cloud tools like GCP/OpenShift and Kubernetes.
Build and maintain scalable data pipelines and lakehouse architectures to collect, process, and govern audio and vehicle telemetry for ML training and in-cabin personalization in automotive infotainment systems.
Build internal UX research tools using React/Angular front-end and Java/Python/Go back-end to help Google teams rapidly turn user insights into product decisions.
Build and scale EarnIn’s real-time financial data infrastructure using AWS, Kafka, Spark, and Python/Scala to power earned-wage insights and analytics for millions of users.
Designs, builds, and optimizes ETL/ELT pipelines and data warehousing solutions to support HR and payroll analytics using SQL, Python, and cloud platforms like AWS Redshift or Snowflake.
Build and maintain ETL/ELT pipelines and data warehouses to power HR analytics and reporting for a cloud-based payroll and HCM platform.
Build distributed data platforms and ML systems that power trading strategies at a quantitative trading firm.
Build and deploy reinforcement-learning models for hyper-personalization and agentic AI use cases to boost customer engagement and conversion at a major telecom/media company.
Build and optimize production-grade LLM systems, integrating commercial APIs and self-hosted models, and implementing RAG pipelines and end-to-end LLM workflows.
Build and optimize distributed ML training/inference pipelines for low-latency trading systems using PyTorch, CUDA, and GPU acceleration.
Build and deploy production-grade LLM chatbots and RAG pipelines using commercial APIs and self-hosted open-source models, optimizing for latency, cost, and reliability.
Build a cross-platform framework for real-time sensing, detection, haptics, and ML inference that powers Nex Playground’s motion-gaming experiences.
Build and deploy machine-learning models to automate decisions and insights for a global BPO provider, using Python, TensorFlow/PyTorch, and cloud platforms.
Build and maintain scalable, real-time data pipelines on AWS and Databricks using Kafka, Spark, Flink, and Airflow, ensuring high-throughput data processing and quality.
Build and optimize data pipelines and storage systems using Spark, Hadoop, Snowflake, and Airflow to process large-scale datasets for analytics and modeling.
Build and maintain scalable data pipelines for machine learning systems, ensuring data quality and security while collaborating with cross-functional teams to deliver AI solutions for clients.
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