MLOps & GenAI Architect

Sourced from tech.jobserve.com

• Define and develop target architectures for MLOps and Generative AI capabilities across cloud environments.
• Design the integration of enterprise data platforms with ML and AI services, particularly across AWS and Databricks.
• Establish end-to-end ML lifecycle frameworks covering feature engineering, model training, model registries, deployment, monitoring, drift detection, and retraining.
• Design GenAI data architectures incorporating embeddings, vector databases, Retrieval Augmented Generation (RAG), and semantic data models.
• Work with Databricks and AWS to develop scalable, AI-ready data products and infrastructure.
• Define architectural standards and controls for responsible AI, data security, privacy, governance, and regulatory compliance.
• Support the integration of GenAI capabilities into enterprise Copilot solutions and AI assistants.