Job Description

- Bachelor s or Master s degree in Computer Science, Data Science, Artificial Intelligence, or a related field. - Proven experience (3+ years) in AI/ML engineering, focusing on generative models. - Strong programming skills in languages such as Python, TensorFlow, and PyTorch. - Familiarity with machine learning frameworks and libraries. - Solid understanding of data structures, algorithms, and software engineering principles. - Experience with cloud platforms like AWS, Google Cloud, or Azure is a plus. - Strong problem-solving skills and ability to work in a fast-paced team environment. - Excellent communication and presentation skills. Design and develop Generative AI applications using advanced NLP techniques, LLMs , and related LLM frameworks. Implement Large Language Models (LLMs) for applications such as language understanding, generation, and conversational AI systems. Build and fine-tune AI, ML, and DL models using frameworks like TensorFlow, PyTorch, and Huggingface . Apply advanced techniques such as transfer learning, fine-tuning pre-trained models, and hyperparameter optimization to improve model performance. Develop and deploy REST APIs using FASTAPI to integrate AI/ML models into applications and services efficiently. Optimize and maintain high-performance endpoints for real-time model inference and scaling. Leverage Databricks and PySpark for scalable processing and model training with large datasets. Ensure optimal performance of machine learning pipelines in a distributed cloud environment. Work closely with data scientists, AI engineers, and product teams to build robust AI solutions. Contribute to the design and development of next-generation Generative AI products and services. Ensure smooth deployment of AI models and services, utilizing cloud infrastructure (e.g ., AWS, Azure, GCP ). Implement monitoring and logging for model performance, scalability, and system health. Stay up to date with the latest advancements in Generative AI, NLP, DL, and LLM frameworks . Contribute to internal RD efforts, exploring new methodologies and technologies to improve AI capabilities.