<p><strong>Responsibilities:</strong></p> <ul> <li>Experience in design, development, and deployment of complex Client models and systems, ensuring they align with business goals and user needs.</li> <li>Able to architect and implement robust, scalable Client solutions, leveraging state-of-the- art techniques and frameworks such as PyTorch, TensorFlow, etc.</li> <li>Fine-tune and optimize large language models such as Mistral, LLaMA, etc., for specific use cases.</li> <li>Implement and experiment with cutting-edge NLP, NLU, and NLG techniques to enhance the capabilities and performance of our conversational AI products.</li> <li>Focused on monitoring and optimizing model performance, ensuring efficiency, accuracy, and fairness in production environments.</li> <li>Collaborate with software engineers to integrate machine learning models into production systems, ensuring scalability, reliability, and performance.</li> <li>Leverage tools and frameworks such as Docker, Kubernetes, ONNX, Kubeflow, MLflow, and other model serving platforms to optimize the deployment and management journey.</li> <li>Interested in staying abreast of the latest advancements in Client research, actively exploring emerging technologies and identifying opportunities for application within the company.</li> <li>Skilled in effectively communicating complex technical concepts to both technical and non-technical audiences, fostering seamless collaboration across teams (Client Engineers, Product Managers, Software Engineers).</li> </ul> <p><strong>Requirements:</strong></p> <ul> <li>A minimum of 2 years of professional experience as a Client Engineer.</li> <li>Bachelor's degree or higher in Computer Science, Machine Learning, AI, Mathematics, or related field.</li> <li>Excellent problem-solving abilities and a pragmatic approach to building scalable and robust machine learning systems.</li> <li>You have a strong foundation in machine learning and deep learning, including embedding methods, supervised and unsupervised learning, and deep learning architectures.</li> <li>Strong programming skills in Python and proficiency with machine learning libraries such as TensorFlow, PyTorch, or JAX.</li> <li>Experience with cloud platforms (e.g., AWS, GCP) and containerization technologies (e.g., Docker, Kubernetes).</li> <li>Candidates must have a strong foundation in statistics and an understanding of machine learning concepts, especially in NLP, NLU and NLG.</li> <li>Familiarity with the MLOps lifecycle, including deployment, monitoring, and orchestration of Client models in production settings.</li> <li>Experience with model deployment tools and platforms like TFServing, TensorRT, TorchServe, ONNX, Kubeflow, and MLflow.</li> <li></li> </ul>