Artificial Intelligence/Machine Learning Engineer

As an experienced AI/ML Developer, your role involves designing, building, and deploying intelligent systems and data-driven models with a strong foundation in machine learning, deep learning, and real-world model deployment. Key Responsibilities: - Design, develop, and deploy machine learning and AI models for business and product use cases. - Work on data preprocessing, feature engineering, model training, validation, and optimization. - Build and maintain scalable ML pipelines for production environments. - Develop predictive models using supervised and unsupervised learning techniques. - Implement deep learning models using frameworks such as TensorFlow or PyTorch. - Collaborate with data engineers, product teams, and backend developers. - Monitor model performance, accuracy, and drift in production. - Document experiments, models, and workflows. Technical Skills Required: - Strong proficiency in Python and ML libraries (NumPy, Pandas, Scikit-learn). - Hands-on experience with deep learning frameworks (TensorFlow, PyTorch). - Solid understanding of machine learning algorithms and statistics. - Experience with NLP, computer vision, or recommendation systems is a plus. - Familiarity with data pipelines, APIs, and model deployment. - Experience with SQL/NoSQL databases and cloud platforms. Good To Have: - Experience with MLOps tools and workflows. - Knowledge of Docker, Kubernetes, and CI/CD for ML deployments. - Exposure to big data technologies. As an experienced AI/ML Developer, your role involves designing, building, and deploying intelligent systems and data-driven models with a strong foundation in machine learning, deep learning, and real-world model deployment. Key Responsibilities: - Design, develop, and deploy machine learning and AI models for business and product use cases. - Work on data preprocessing, feature engineering, model training, validation, and optimization. - Build and maintain scalable ML pipelines for production environments. - Develop predictive models using supervised and unsupervised learning techniques. - Implement deep learning models using frameworks such as TensorFlow or PyTorch. - Collaborate with data engineers, product teams, and backend developers. - Monitor model performance, accuracy, and drift in production. - Document experiments, models, and workflows. Technical Skills Required: - Strong proficiency in Python and ML libraries (NumPy, Pandas, Scikit-learn). - Hands-on experience with deep learning frameworks (TensorFlow, PyTorch). - Solid understanding of machine learning algorithms and statistics. - Experience with NLP, computer vision, or recommendation systems is a plus. - Familiarity with data pipelines, APIs, and model deployment. - Experience with SQL/NoSQL databases and cloud platforms. Good To Have: - Experience with MLOps tools and workflows. - Knowledge of Docker, Kubernetes, and CI/CD for ML deployments. - Exposure to big data technologies.