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Python and AI/ML libraries.\nHands-on experience with PyTorch, TensorFlow, and JAX.\nExperience with HuggingFace Transformers and Diffusers ecosystem.\nExpertise in LangChain, LangGraph, LlamaIndex, and Semantic Kernel.\nPractical exposure to MLflow, Kubeflow, and Weights and Biases.\n\nGenerative AI and LLMs: \nStrong experience with RAG architectures and Vector Databases (FAISS, Pinecone, Weaviate, Milvus).\nExperience with Multi-Agent orchestration and OpenAI APIs.\nExpertise in LLM fine-tuning techniques such as LoRA and QLoRA.\nAdvanced Prompt Engineering and AI safety alignment knowledge.\n\nMachine Learning and Deep Learning: \nStrong foundation in Classical ML and Deep Learning models.\nExperience with CNNs, Transformers, Time Series Forecasting, and Graph Neural Networks.\nExposure to Reinforcement Learning concepts and architectures.\n\nComputer Vision: \nExperience with YOLO, DETR, Faster R-CNN, OCR frameworks, and Video Analytics.\nFamiliarity with Multi-modal AI models like CLIP, BLIP, and Segment Anything (SAM).\n\nOptimisation and Deployment: \nExpertise in TensorRT, ONNX Runtime, Quantisation, and Model Optimisation.\nExperience deploying Dockerized microservices on Kubernetes.\nDeep expertise in Azure AI/ML services, including AKS, Azure OpenAI, AML, Azure Functions, etc.\nFamiliarity with AWS SageMaker and GCP Vertex AI is an added advantage.\nExperience with monitoring tools such as Prometheus, Grafana, and ELK Stack.\n\nGood to Have: \nBachelor's/Master's degree in Computer Science or related field.\n6+ years of experience in AI/ML with proven production deployment experience.\nStrong understanding of ML/DL concepts, neural networks, and optimisation techniques.\nExcellent analytical, problem-solving, and debugging skills.\nOpen-source contributions, publications, or patents will be an added advantage.\nSkills\nAKS, Azure, Azure ML, Azure OpenAI, Kubeflow, LLMOps, MLOps, MLflow, Microsoft Azure, 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