Job Description:

• Collect and prepare data for training and evaluating multimodal foundation models. This may involve cleaning and processing text data or creating synthetic data.
• Develop and optimize large-scale language models like GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders)
• Work on tasks involving language modeling, text generation, understanding, and contextual comprehension.
• Regularly review and fine-tune Large Language models to ensure maximum accuracy and relevance for custom datasets.
• Build and deploy AI applications on cloud platforms – any hyperscaler Azure, GCP or AWS.
• Integrate AI models with our company's data to enhance and augment existing applications.

Role & Responsibility

• Handle data preprocessing, augmentation, and generation of synthetic data.
• Design and develop backend services using Python or .NET to support OpenAI-powered solutions (or any other LLM solution)
• Develop and Maintaining AI Pipelines
• Work with custom datasets, utilizing techniques like chunking and embeddings, to train and fine-tune models.
• Integrate Azure cognitive services (or equivalent platform services) to extend functionality and improve AI solutions
• Collaborate with cross-functional teams to ensure smooth deployment and integration of AI solutions.
• Ensure the robustness, efficiency, and scalability of AI systems.
• Stay updated with the latest advancements in AI and machine learning technologies.

Skills & Experience

• Strong foundation in machine learning, deep learning, and computer science.

• Expertise in generative AI models and techniques (e.g., GANs, VAEs, Transformers).

• Experience with natural language processing (NLP) and computer vision is a plus.

• Ability to work independently and as part of a team.

• Knowledge of advanced programming like Python, and especially AI-centric libraries like TensorFlow, PyTorch, and Keras. This includes the ability to implement and manipulate complex algorithms fundamental to developing generative AI models.

• Knowledge of Natural language processing (NLP) for text generation projects like text parsing, sentiment analysis, and the use of transformers like GPT (generative pre-trained transformer) models.

• Experience in Data management, including data pre-processing, augmentation, and generation of synthetic data. This involves cleaning, labeling, and augmenting data to train and improve AI models.

• Experience in developing and deploying AI models in production environments.

  • Knowledge of cloud services (AWS, Azure, GCP) and understanding of containerization technologies like Docker and orchestration tools like Kubernetes for deploying , managing and scaling AI solutions, 

    Should be able to bring new ideas and innovative solutions to our clients