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The ideal candidate will also have hands-on exposure to Generative AI (GenAI) and the ability to design, implement, and optimize advanced AI-driven solutions for real-world business problems. \n\nThis role requires both technical depth and strategic thinking, with an ability to lead projects, collaborate with cross-functional teams, and deliver scalable, impactful AI solutions. \n\nKey Responsibilities\n\nDesign, develop, and deploy end-to-end machine learning and AI models for diverse business use cases. \n\nApply advanced deep learning architectures (CNNs, RNNs, Transformers, etc.) for solving complex problems in vision, text, and structured/unstructured data. \n\nDevelop and fine-tune NLP models for text classification, sentiment analysis, information extraction, conversational AI, and semantic search. \n\nExplore and implement Generative AI (LLMs, diffusion models, prompt engineering, fine-tuning, RAG pipelines, etc.) to enhance existing AI/ML capabilities. \n\nCollaborate with product, engineering, and business stakeholders to translate requirements into AI-driven solutions. \n\nDrive model performance optimization (scalability, latency, explainability, accuracy). \n\nLead and mentor junior data scientists, providing technical guidance and best practices. \n\nStay up to date with emerging AI/ML and GenAI frameworks, tools, and research trends. \n\nRequired Skills & Experience\n\nHaving 6+ years of experience in Data Science, AI/ML, and related fields. \n\nStrong proficiency in Python, R, or similar languages with expertise in data science libraries (NumPy, Pandas, Scikit-learn, PyTorch, TensorFlow, Hugging Face). \n\nProven experience in designing and deploying ML/DL models in production. \n\nDeep knowledge of NLP techniques: embeddings, Transformers, BERT/GPT-style models, seq2seq, text generation, entity recognition. \n\nHands-on exposure to Generative AI (e.g., OpenAI, Hugging Face, LangChain, vector databases, RAG, LLM fine-tuning). \n\nStrong knowledge of cloud platforms (AWS, Azure, GCP) for ML/AI model deployment. \n\nSolid understanding of MLOps practices (model versioning, monitoring, CI/CD pipelines). \n\nStrong analytical, problem-solving, and communication skills. \n\nAbility to work in a fast-paced, collaborative environment. \n\nPreferred Qualifications\n\nMaster’s/PhD in Computer Science, Data Science, AI/ML, Statistics, or a related field. \n\nExperience in GenAI enterprise use cases (chatbots, content generation, code generation, knowledge retrieval). \n\nKnowledge of big data tools (Spark, Databricks, Hadoop) is a plus. \n\nExperience mentoring or leading a small data science team. \n\nOriginally posted on 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