Generative AI Engineer

AI & ML Engineer Role Overview We are looking for an AI & ML Engineer with strong hands-on experience in Python, PyTorch, Generative AI, RAG, and modern model serving technologies. The ideal candidate should have experience building, deploying, monitoring, and optimizing machine learning models in cloud environments, particularly GCP. Key Responsibilities

• Develop and implement AI/ML solutions using Python and PyTorch.
• Work with Transformer architectures and modern Generative AI/LLM technologies.
• Design and implement Agent architectures, including approaches such as ReAct.
• Build and optimize RAG (Retrieval-Augmented Generation) solutions.
• Package and serve ML models using Docker containers.
• Deploy models using KServe and Vertex AI endpoints.
• Work with feature registries and model registries.
• Implement and maintain CI/CD pipelines for ML model deployment.
• Set up model monitoring, performance tracking, and drift detection.
• Implement cost-efficient autoscaling and GPU scheduling strategies.
• Support canary and shadow deployments for ML models.
• Implement A/B testing to evaluate model performance and improvements.
• Work with cloud databases and GCP-based ML infrastructure. Required Skills
• Strong Python programming skills.
• Hands-on experience with PyTorch.
• Good understanding of Transformer architecture.
• Experience with Generative AI / LLMs and RAG.
• Experience designing AI Agent architectures, such as ReAct.
• Strong knowledge of GCP.
• Hands-on experience with Docker and containerization.
• Experience with cloud databases.
• Experience with KServe and/or Vertex AI endpoints.
• Knowledge of model and feature registries.
• Experience with ML CI/CD and MLOps practices.
• Experience with model monitoring and drift detection. Additional / Preferred Experience
• Cost-efficient model autoscaling.
• GPU scheduling and optimization.
• Canary deployments and shadow deployments.
• A/B testing for ML models.
• Production deployment and lifecycle management of AI/ML models.