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probability, hypothesis testing, experimentation, feature engineering, and model evaluation.\nExperience with Generative AI, Large Language Models (LLMs), Prompt Engineering, RAG, embeddings, vector databases, and AI agents.\nExperience building and deploying production-grade AI/ML solutions using cloud platforms such as Azure or AWS.\nHands-on experience with Azure AI Foundry / Azure OpenAI / Amazon SageMaker or equivalent AI/ML platforms.\nExperience designing data pipelines and ML pipelines for training, validation, deployment, and monitoring.\nExperience with MLOps, including model versioning, experiment tracking, CI/CD, model deployment, monitoring, and model drift detection.\nStrong experience working with SQL and relational databases and familiarity with data warehouses/data lakes.\nExperience with REST APIs, microservices, Docker, and Kubernetes for AI/ML application deployment.\nImplement AI/ML solutions with appropriate security, privacy, responsible AI, and governance controls.\nOptimize models for accuracy, scalability, latency, reliability, and cost.\nLead technical discussions and mentor Data Scientists, ML Engineers, and junior team members.\n\n8+ years of experience in Data Science / Machine Learning / AI.\nStrong programming experience in Python.\nStrong knowledge of Machine Learning and Deep Learning algorithms.\nStrong understanding of statistics and mathematical foundations of ML.\nHands-on experience with Generative AI and LLM applications.\nExperience with RAG architectures, embeddings, vector databases, and semantic search.\nExperience with AI Agents / Agentic AI architectures.\nExperience with Azure AI Foundry, Azure OpenAI, AWS SageMaker, or equivalent platforms.\nStrong experience with ML model development, evaluation, deployment, and monitoring.\nExperience with MLOps and ML lifecycle management.\nStrong SQL and data manipulation skills.\nExperience with Git and CI/CD.\nExperience with Docker and Kubernetes is preferred.\nExperience 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