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Lead the vision for personalization algorithms at a global D2C brand, designing and deploying real-time ML models to drive measurable customer and business impact.
Designs and deploys production-grade AI systems, including multi-agent workflows and RAG, on GCP while mentoring engineers and enforcing engineering standards.
Build and maintain automated testing and CI/CD infrastructure for a complex AI/ML software stack, ensuring reliability and performance across simulators, emulators, and hardware.
Lead AI Engineer builds and deploys generative AI and agentic systems for financial services, focusing on multi-modal LLMs, scalable MLOps pipelines, and enterprise integration.
Designs scalable AI systems and governance for large enterprises, aligning business needs with cloud, data, and security architectures.
Design and lead ML architecture for sports-tech products, building scalable inference pipelines and real-time 3D systems that power immersive fan experiences.
Design and sell AI-powered data solutions for customers, integrating data platforms, AI models, and agentic frameworks to enable scalable, secure AI deployments across industries.
Build and deploy production-scale NLP and personalization models that process billions of consumer reviews and UGC, using Python, cloud ML stacks, and LLMs.
Principal Data Engineer builds secure, scalable data pipelines and platforms for a defence client’s Secure Digital Platform, using cloud tools like Azure/AWS and Python/Spark.
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.
Build, train, and deploy ML/DL models (including LLMs) for clients, using Python, SQL, Spark, and cloud platforms, while following MLOps practices.
Build and scale ML-powered recommendation and personalization systems for a cannabis retail platform, designing ranking, retrieval, and forecasting models to improve eCommerce experiences.
Lead hands-on Data Scientist to build and launch the company’s first ML product—personalization and recommendation systems—then scale the Data Science function across e-commerce use cases like forecasting and optimization.
Design and deploy production-grade GenAI and ML solutions on AWS, optimizing cost, security, and performance while embedding reusable patterns into DoiT’s Cloud Intelligence platform.
Design and deploy production-grade GenAI and ML solutions on AWS for enterprise customers, focusing on cost efficiency, reliability, and security while creating reusable patterns and driving product adoption.
Lead a team building and scaling Azure-based data pipelines and AI-ready data models for a large ecommerce retailer using PySpark, Databricks, and Data Factory.
Мы ищем ML-инженера , который будет разрабатывать и внедрять модели машинного обучения для задач прогнозирования. Вы присоединитесь к команде, которая строит системы, помогающие бизнесу принимать решения на основе…
Build and maintain backend systems and AI-driven automation for hospital billing and auditing workflows in Brazil’s healthcare sector.
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