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Designs and deploys ML models for localization workflows using Python, TensorFlow, and AWS services; owns projects from conception to production.
Lead AI and data-science engagements for retail clients, translating business needs into AI solutions and deploying models using modern frameworks and cloud platforms.
Build and test Reinforcement Learning Environments for AI systems using Rust, C++, Python, or other languages; tasks include debugging, refactoring, and performance optimization.
Build and validate AI/ML models (LLMs, NLP, CV) for banking use cases, rapidly prototype solutions, and mentor junior team members in a collaborative, onsite environment.
Manage AI/ML projects end-to-end, coordinating cross-functional teams to deploy classification, detection, and predictive models in production using Agile/Scrum and Jira/Confluence.
Build and maintain AI/ML pipelines and data infrastructure to support classification, detection, and predictive models using Python, Spark, and cloud data services.
Build and optimize OCR and handwriting-recognition models for an EdTech platform, turning student essays into structured text and improving teacher feedback workflows.
Lead a team building enterprise-grade AI solutions for healthcare, deploying LLMs on cloud platforms like AWS/Azure with OpenShift and Kubernetes while ensuring security, compliance, and scalability.
Business Development Manager at DeepLLMData, sourcing clients and partnerships for AI/LLM model training services, crafting proposals, and collaborating with technical teams.
Attend a global AI conference in Dubai focused on deep learning, reinforcement learning, and machine learning research presentations and networking.
Build and deploy cutting-edge AI models, including LLMs, by collaborating with researchers to pre-train, fine-tune, and evaluate large-scale machine learning systems.
Build ML/AI models to optimize asset-management strategies and fintech products using blockchain, tokenization, and NFT data.
Lead the design and deployment of advanced AI models, including LLMs and multimodal systems, using deep learning and statistical methods to solve complex business and research challenges.
Build and deploy production-grade AI/ML models using Python, TensorFlow, PyTorch, and MLOps tooling for scalable, real-world applications.
Build and scale AI agents and ML pipelines using Python, PyTorch/TensorFlow, and frameworks like LangChain; integrate LLMs, vector DBs, and cloud-native systems.
Build and deploy production-grade ML models and RAG pipelines to replace rule-based systems, focusing on model quality, evaluation, and self-hosted LLM inference.
Build and scale the backend platform that powers AI-driven travel experiences, integrating ML models and vector search for real-time personalization at global scale.
Design and document real-world DevOps/backend incidents (outages, scaling, security) to train AI systems. Work with Kubernetes, CI/CD, observability, and IaC.
Build, optimize, and integrate AI models (CV/NLP) into banking systems using Python, TensorFlow/PyTorch, and MLOps pipelines.
Build and scale production-grade LLM applications and AI systems for enterprises, owning architecture, data pipelines, deployment, and MLOps with Python, PyTorch/TensorFlow, and cloud-native tools.
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