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Design data models and pipelines for AI workloads, implement MLOps automation, and deploy scalable batch/streaming systems to support agentic workflows.
Build and deploy AI/ML pipelines using Python, FastAPI, and cloud services like GCP to serve vector-based models and microservices.
Build and maintain ML models and analytics pipelines in Python/SQL on GCP, then deploy and monitor them using MLOps practices and tools like Bokeh and Git.
Build and maintain data pipelines, models, and segments for National Geographic’s subscription business, using SQL, Python, and cloud warehouses to power marketing and personalization.
Build and deploy generative AI and document AI solutions using LLMs, RAG, and agentic frameworks in Python, then transfer ownership to business teams.
Build and optimize large-scale AI/ML systems for Google Cloud, leveraging distributed computing and advanced algorithms to power services like Search and YouTube.
Build and deploy AI-powered audience analytics for a large-scale media intelligence platform, using Python, PySpark, Databricks, and modern ML/AI techniques.
Designs and deploys production-grade AI systems, including multi-agent workflows and RAG, on GCP while mentoring engineers and enforcing engineering standards.
Lead a team to build and ship scalable generative AI applications using Google’s latest models, translating research into real-world products while mentoring engineers.
Design and lead ML architecture for sports-tech products, building scalable inference pipelines and real-time 3D systems that power immersive fan experiences.
Build and deploy production-scale NLP and personalization models that process billions of consumer reviews and UGC, using Python, cloud ML stacks, and LLMs.
Lead AI Engineer designs, builds, and deploys enterprise-scale AI/ML and Generative AI systems, including RAG pipelines and agentic workflows, across Azure, GCP, or AWS.
Design and deploy AI-powered applications, including LLM-based agents and workflows, to transform enterprise processes while ensuring scalability, security, and governance.
Build and scale Ultralytics HUB, a platform for AI model development using Python, FastAPI, TypeScript, and Nuxt.js, deployed on GCP with Docker and microservices.
Leads AI engineering at Weekday AI, designing and scaling core AI models and infrastructure for the company’s primary product.
Builds and maintains Python-based backend services for a real-time situational awareness platform, integrating AI models (LLMs, RAG, Computer Vision) and cloud-native tools (K8s, AWS) to support emergency response and defense operations.
Lead cross-functional creative-technology programs for Google’s brand experiences, from AI-powered events to digital campaigns, managing timelines, cloud resources, and technical risks.
Lead a team of engineers to design and optimize Google’s AI/ML infrastructure, including model deployment and distributed computing, while aligning technical strategy with business goals.
Design, build, and deploy AI/ML models and services, including generative AI and RAG systems, while ensuring responsible AI practices and robust MLOps pipelines.
Build and maintain scalable data pipelines, vector stores, and LLM-driven BI systems using Python, Spark, and cloud ML services to power real-time AI applications.
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