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Skills: Artificial Intelligence, Machine Learning. Experience: 4 - 10 Years. Qualification: Bachelor Of Engineering\n\nDesired Competencies\n(Technical/Behavioral Competency)\n\nMust-Have\n\nStrong\nhands-on experience in Python for AI/ML development. \nExperience in building, training, validating, and deploying Machine\nLearning models using libraries such as NumPy, Pandas, Scikit-learn,\nor similar. \nHands-on experience in data preprocessing, feature engineering, model\nselection, model evaluation, and performance tuning. \nGood understanding of ML algorithms such as classification, regression,\nclustering, recommendation systems, forecasting, or similar. \nExperience in NLP techniques such as text classification, entity\nextraction, sentiment analysis, embeddings, semantic search, or document\nprocessing. \nExposure to Generative AI / LLM-based solutions, prompt engineering,\nembeddings, semantic search, RAG-based solutions, or AI application\ndevelopment. \nGood knowledge of SQL and ability to work with structured and\nunstructured data from multiple sources. \nExperience with model deployment, API integration, monitoring, and\nproduction support. \nGood understanding of MLOps concepts, including model versioning,\nexperiment tracking, CI/CD for ML, monitoring, retraining, and model\nlifecycle management. \nStrong analytical, problem-solving, and communication skills with ability to\nwork with business, data, engineering, and product teams.\n\nGood-to-Have\n\nExperience in Generative AI\nframeworks and tools, including LangChain, LangGraph, AutoGen, or\nsimilar frameworks. \nHands-on experience in Large Language Models, transformer-based systems,\nLLM-integrated pipelines, and GenAI application development. \nExposure to GANs, diffusion models, and advanced generative AI\ntechniques. \nHands-on experience with deep learning frameworks such as TensorFlow,\nPyTorch, or Keras. \nExperience building APIs using Flask, FastAPI, or similar Python-based\nAPI frameworks. \nExperience in AI/ML use cases across Generative AI, NLP, Computer Vision,\nIndustrial Analytics, automation, or human-computer interaction. \nExperience building scalable ML pipelines covering data ingestion,\npreprocessing, model training, evaluation, deployment, monitoring, and\nretraining. \nExposure to cloud platforms such as Azure, AWS, or GCP and related\nAI/ML services. \nExposure to Azure OpenAI, AWS Bedrock, Google Vertex AI, OpenAI APIs,\nor similar AI platforms. \nExperience with vector databases such as FAISS, Pinecone, Chroma,\nWeaviate, Azure AI Search, or similar. \nExperience in developing RAG pipelines, chatbots, document intelligence\nsolutions, knowledge assistants, or enterprise AI applications. \nExposure to Docker, Kubernetes, MLflow, Airflow, Kubeflow, Databricks,\nor similar tools. \nUnderstanding of model robustness, explainability, fairness, privacy,\nsecurity, and data governance standards. \nMust be well versed or have exposure to Agile methodology and software\nengineering best 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