Machine Learning Engineer

As an ML Engineer at HolboxAI, you will be responsible for designing, developing, and implementing Machine Learning models, including Deep Learning and Natural Language Processing. Key Responsibilities: - Working with large datasets to perform data cleaning, preprocessing, feature engineering, and analysis. - Developing and deploying AI/ML models on Cloud platforms. - Experimenting with and implementing Generative AI models and techniques for various applications. - Contributing to the development of Agentic AI systems and workflows. - Evaluating the performance of AI/ML models and identifying areas for improvement. - Collaborating with Data Engineers to build and maintain data pipelines for AI/ML applications. - Staying up-to-date with the latest advancements in AI and related technologies. - Documenting code, models, and processes clearly and effectively. - Working closely with product managers and other stakeholders to understand requirements and deliver impactful solutions. - Participating in code reviews and contributing to maintaining high code quality. Qualifications Required: - Minimum of 2-6 years of hands-on experience in developing and deploying AI/ML solutions. - Strong hands-on experience with Cloud platforms (e.g., AWS, Azure, GCP) for AI/ML workloads. - Deep hands-on experience in Data Science methodologies and techniques. - Extensive hands-on experience in developing and deploying Machine Learning models using various frameworks (e.g., TensorFlow, PyTorch, scikit-learn). - Proven hands-on experience with Generative AI models and techniques (e.g., Large Language Models, Diffusion Models). - Practical hands-on experience in building and deploying Agentic AI systems. - Strong programming skills in Python and experience with relevant libraries (e.g., NumPy, Pandas, scikit-learn, TensorFlow, PyTorch, Transformers). - Solid understanding of statistical modeling and machine learning algorithms. - Experience with AI tools and techniques. - Excellent problem-solving and analytical skills. - Good communication and collaboration skills. - AWS hands-on work experience is a plus.,