Job Description

Key Responsibilities Design, develop, and deploy machine learning models, with a focus on large language models (LLMs), ensuring scalability and performance. Handle large datasets, perform preprocessing, feature engineering, and prepare data pipelines for training. Fine-tune pre-trained LLMs (e.g., GPT, BERT, etc.) for specific use cases, ensuring optimal performance. Stay updated with the latest trends in AI/ML, particularly in LLMs and deep learning, to propose innovative solutions. Continuously test, monitor, and optimize models for accuracy, speed, and efficiency. Maintain clear documentation for model development, testing procedures, and performance metrics. Integrate AI models and LLM such as those from Microsoft Azure, OpenAI, Google Cloud, and AWS into scalable and robust applications. Build and maintain RESTful APIs and microservices to support AI functionalities. Implement front-end components using modern frameworks (e.g., React, Angular, Vue.js) and ensure responsive design and user experience. Write clean, maintainable, and efficient code while adhering to best practices in software development. Troubleshoot and resolve issues, ensuring high availability and performance of AI-enabled applications. Desired Experience Bachelor s degree in Computer Science, Engineering, or a related field. Proven experience with a strong portfolio of AI-enabled products (1+ year). Proficiency in front-end technologies such as HTML, CSS, JavaScript, and modern frameworks (React, Angular, Vue.js). Expertise in back-end development using languages such as Python and Node.js. Hands-on experience with cloud services and AI APIs such as those from Microsoft Azure, OpenAI, Google Cloud, and AWS. Knowledge of database technologies (SQL, NoSQL) and data modeling. Strong problem-solving skills and ability to work in a fast-paced, collaborative environment. Excellent communication skills and the ability to articulate technical concepts to non-technical stakeholders. Uses AI tools and automation to boost code generation, debugging, and deployment. Desired Skills Strong knowledge of machine learning frameworks (e.g., TensorFlow, PyTorch, Hugging Face, etc.) Proficiency in Python, with experience using libraries like NumPy, pandas, scikit-learn, etc. Experience with model fine-tuning and transfer learning in LLMs. Familiarity with cloud platforms (AWS, Google Cloud, or Azure) for model deployment. Knowledge of NLP concepts such as tokenization, attention mechanisms, embeddings, etc. Problem-Solving: Strong analytical and problem-solving skills, with the ability to troubleshoot and optimize machine learning models. Communication: Excellent communication skills to collaborate with teams and present complex ideas clearly.