Generative AI Engineer

W2 Candidates Only

📍 Location: USA
🛂 Visa: Open to any visa type with valid work authorization in the USA

💼 Experience Required: 6 to 12 years
📊 Level: Mid to Lead positions

Key Responsibilities

• Model Development: Design and implement generative models, including GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), and large language models (LLMs) like GPT or BERT.
• Data Management: Collect, preprocess, and augment large datasets to train generative models, ensuring data quality and relevance.
• Model Optimization: Tune hyperparameters and employ techniques like model pruning and quantization to enhance model performance and efficiency.
• Deployment & Integration: Deploy generative models into production environments, ensuring scalability and reliability. Integrate models with existing systems and workflows.
• Collaboration: Work closely with cross-functional teams, including data scientists, software engineers, and product managers, to align AI solutions with business objectives.
• Research & Innovation: Stay updated with the latest advancements in generative AI technologies and methodologies. Contribute to research and development efforts to drive innovation within the organization.

Required Skills & Qualifications

• Education: Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or a related field.
• Programming Languages: Proficiency in Python, with experience in ML libraries such as TensorFlow, PyTorch, and Keras.
• Machine Learning Expertise: Strong foundation in machine learning algorithms, deep learning techniques, and model evaluation metrics.
• Data Handling: Experience with data preprocessing, augmentation, and management of large-scale datasets.
• Model Deployment: Familiarity with deploying models on cloud platforms (AWS, GCP, Azure) and integrating them into production systems.
• Communication Skills: Ability to communicate complex technical concepts to non-technical stakeholders.