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AI/ML Lead

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Summary

Lead AI/ML role in Noida, India: owns design, training, deployment, and monitoring of ML/deep-learning models, builds Generative AI/LLM/RAG solutions, implements MLOps, and mentors engineers. Core stack: Python, TensorFlow/PyTorch, scikit-learn, FastAPI/Flask, Docker/Kubernetes, AWS/Azure/GCP.

  • Lead the design, development, testing, and deployment of AI/ML models and solutions.

  • Define AI/ML strategies and technical roadmaps aligned with business objectives.

  • Build and optimize machine learning and deep learning models for real-world applications.

  • Oversee data preprocessing, feature engineering, model training, validation, and evaluation.

  • Work with algorithms for classification, regression, clustering, and predictive analytics.

  • Explore and implement Generative AI, Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG) solutions.

  • Develop scalable AI pipelines and production-ready ML applications.

  • Implement MLOps practices for model deployment, monitoring, versioning, and retraining.

  • Collaborate with data engineers, software developers, product managers, and business stakeholders.

  • Mentor and guide AI/ML engineers and ensure high-quality technical delivery.

  • Monitor model performance, accuracy, scalability, and reliability.

  • Ensure data privacy, model security, responsible AI practices, and compliance.

  • Evaluate emerging AI technologies and recommend suitable tools and frameworks.

  • Communicate project progress, technical insights, and business impact to stakeholders.



Requirements

  • Strong programming skills in Python.

  • Expertise in machine learning libraries such as Scikit-learn, TensorFlow, and PyTorch.

  • Strong understanding of statistics, probability, linear algebra, and optimization.

  • Experience with data processing and analysis using Pandas, NumPy, and SQL.

  • Knowledge of deep learning, NLP, and computer vision.

  • Experience with Generative AI, LLMs, prompt engineering, and RAG architectures.

  • Familiarity with frameworks such as LangChain or LlamaIndex.

  • Experience with model deployment using FastAPI, Flask, or REST APIs.

  • Knowledge of MLOps tools, Docker, Kubernetes, and CI/CD pipelines.

  • Familiarity with cloud platforms such as AWS, Microsoft Azure, or Google Cloud.

  • Strong leadership, problem-solving, communication, and project management skills.



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