Senior Executive- AI ML Engineer

Role & responsibilities Design, develop, and implement machine learning models and algorithms to address business challenges. Work with data scientists, engineers, and stakeholders to understand requirements and translate them into technical solutions. Perform data preprocessing, feature engineering, and exploratory data analysis on large datasets. Train, validate, and deploy machine learning and deep learning models using frameworks such as TensorFlow, PyTorch, or scikit-learn. Optimize model performance, including hyperparameter tuning and debugging. Develop and maintain APIs and pipelines to integrate ML models into production systems. Stay updated with the latest advancements in AI/ML research and incorporate relevant technologies into ongoing projects. Create technical documentation for models, processes, and systems. Collaborate with cross-functional teams to deliver end-to-end AI/ML solutions. Preferred candidate profile Strong proficiency in Python, including libraries such as NumPy, Pandas, and scikit-learn. Hands-on experience with machine learning frameworks like TensorFlow, PyTorch, or Keras. Solid understanding of supervised, unsupervised, and reinforcement learning algorithms. Experience with NLP (Natural Language Processing), Computer Vision, or time-series analysis, Predictive Analytics. Proficiency in data preprocessing, feature selection, and model evaluation techniques. Familiarity with cloud platforms (e.g., AWS, Azure, Google Cloud) and MLOps tools. Experience with distributed computing tools like Hadoop or Spark. Strong knowledge of RESTful API design and model deployment strategies (e.g., Flask, FastAPI,or Docker). Excellent problem-solving skills and ability to work independently or collaboratively. Role & responsibilities Design, develop, and implement machine learning models and algorithms to address business challenges. Work with data scientists, engineers, and stakeholders to understand requirements and translate them into technical solutions. Perform data preprocessing, feature engineering, and exploratory data analysis on large datasets. Train, validate, and deploy machine learning and deep learning models using frameworks such as TensorFlow, PyTorch, or scikit-learn. Optimize model performance, including hyperparameter tuning and debugging. Develop and maintain APIs and pipelines to integrate ML models into production systems. Stay updated with the latest advancements in AI/ML research and incorporate relevant technologies into ongoing projects. Create technical documentation for models, processes, and systems. Collaborate with cross-functional teams to deliver end-to-end AI/ML solutions. Preferred candidate profile Strong proficiency in Python, including libraries such as NumPy, Pandas, and scikit-learn. Hands-on experience with machine learning frameworks like TensorFlow, PyTorch, or Keras. Solid understanding of supervised, unsupervised, and reinforcement learning algorithms. Experience with NLP (Natural Language Processing), Computer Vision, or time-series analysis, Predictive Analytics. Proficiency in data preprocessing, feature selection, and model evaluation techniques. Familiarity with cloud platforms (e.g., AWS, Azure, Google Cloud) and MLOps tools. Experience with distributed computing tools like Hadoop or Spark. Strong knowledge of RESTful API design and model deployment strategies (e.g., Flask, FastAPI,or Docker). Excellent problem-solving skills and ability to work independently or collaboratively.