Data Architect - Generative AI

Key Responsibilities

• Define and architect scalable solutions involving Generative AI, Retrieval-Augmented Generation (RAG), and LLMOps workflows.
• Lead the design and implementation of microservices-based, event-driven architectures leveraging REST APIs and API governance best practices.
• Collaborate with global teams and stakeholders to ensure architectural alignment and successful delivery of solutions to production.
• Provide strong technical leadership and mentorship to development teams.
• Hands-on development using Python frameworks such as Django, Flask, or FastAPI when required.
• Implement and optimize GenAI solutions utilizing Vertex AI, OpenAI, vector databases, and other LLM-based models.
• Leverage GCP technologies including Cloud Build, AutoML, Cloud Code, and foundational cloud services to build robust AI architectures.
• Apply Object-Oriented Analysis and Design (OOAD) principles in system design.
• Understand and integrate frontend technologies such as JavaScript and HTML5 when necessary.
• Quickly adapt to new tools, technologies, and evolving AI trends to drive innovation and improvement.
  

Mandatory Skills

• Python programming and frameworks (Django, Flask, FastAPI)
• Vertex AI and Google Cloud Platform (GCP) fundamentals including Cloud Build, AutoML, and Cloud Code
• Generative AI, OpenAI, LLM-based models, and RAG workflows
• Experience with Vector Databases and search solutions
• Object-Oriented Analysis and Design (OOAD)
• Strong knowledge of microservices, event-driven architectures, REST APIs, and API governance
  

Preferred Skills

• Azure cloud expertise (AZ-304 or AZ-305 certification is a plus)
• Hands-on experience with NoSQL databases such as Cosmos DB or MongoDB
• Excellent communication and collaboration skills, with experience working in global, cross-functional teams
• Strong problem-solving ability and a passion for mentoring teams