AI/ML Lead

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

  
  


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

  
  


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

  
  


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

  
  


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

  
  


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

  
  


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  Develop scalable AI pipelines and production-ready ML applications.
  
  

  
  


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

  
  


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

  
  


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

  
  


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  Monitor model performance, accuracy, scalability, and reliability.
  
  

  
  


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

  
  


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  Evaluate emerging AI technologies and recommend suitable tools and frameworks.
  
  

  
  


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

  
  

Requirements

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  Strong programming skills in Python.
  
  

  
  


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

  
  


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

  
  


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

  
  


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  Knowledge of deep learning, NLP, and computer vision.
  
  

  
  


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

  
  


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  Familiarity with frameworks such as LangChain or LlamaIndex.
  
  

  
  


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

  
  


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

  
  


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

  
  


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