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Build and deploy AI systems including LLMs, computer vision, and autonomous agents using Python, PyTorch, and LangChain, then productionize them with MLOps on cloud platforms.
Designs the architecture for self-learning AI agents and GenAI systems, focusing on orchestration, reasoning layers, and scalable agentic workflows.
Build and scale AI agents and ML pipelines using Python, PyTorch/TensorFlow, and frameworks like LangChain; integrate LLMs, vector DBs, and cloud-native systems.
Lead a team to design, build, and maintain scalable data pipelines and warehouses, ensuring reliable analytics and ML-ready datasets using SQL, Python, and cloud tools.
Design and deploy AI/ML models, including LLMs and GenAI, to solve healthcare data challenges using Python, cloud platforms, and MLOps.
Design and deploy AI-powered applications on Azure using Azure AI Services, Azure OpenAI, and Copilot Studio to build chatbots and enterprise copilots.
Design and deploy scalable AI systems, lead architecture decisions, and ensure alignment with business goals using cloud-native tools and MLOps best practices.
Design, build, and deploy AI-powered applications on Azure using Azure AI Services, Azure OpenAI, Copilot Studio, and Power Platform to create chatbots, knowledge assistants, and enterprise copilots.
Build and deploy AI-powered applications using LLMs, RAG pipelines, and vector search. Integrate LLM APIs into production systems with Python and cloud tools.
Build, optimize, and deploy AI/ML models (including LLMs) using Python, TensorFlow/PyTorch, and cloud platforms in a product-focused team.
Builds scalable data pipelines and AI/ML models in Python/SQL, deploys them via MLOps, and maintains cloud-based data infrastructure for intelligent applications.
Designs the architecture for self-learning AI agents and GenAI systems, focusing on orchestration, reasoning layers, and scalable production pipelines.
Build and maintain scalable data pipelines and warehouses using SQL, Python, and cloud tools (AWS/GCP/Azure) to feed analytics and ML workloads.
Build and integrate AI-powered features for a large automotive marketplace, using LLMs, RAG, and cloud services to improve search, recommendations, and automation.
Build and integrate AI-powered features for a large automotive marketplace, using LLMs, RAG, and cloud services to automate workflows and improve user experience.
Build production-grade AI agents and LLM-powered features like chatbots and RAG systems using Python, FastAPI, and third-party models (OpenAI, Gemini, Claude).
Build and deploy AI-powered Python applications, REST APIs, and ML pipelines using TensorFlow/PyTorch, Docker, and cloud AI services.
Owns Kubernetes clusters, GitLab CI/CD, and Airflow pipelines to keep data workflows reliable and SLA-compliant.
Build and automate global ML pipelines for predictive analytics in insurance, collaborating with teams to translate insights and scale models.
Designs and maintains AI-driven data pipelines and MLOps workflows in Python, optimizing data processing for ML initiatives.
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