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.

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.

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

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.

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.

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

Gen AI Engineer

Skills