Data Architect

We are seeking an experienced GCP AI Architect to lead the design, architecture, and implementation of enterprise-scale Artificial Intelligence (AI), Generative AI, Machine Learning (ML), and Data Analytics solutions on Google Cloud Platform (GCP). The ideal candidate will combine deep expertise in cloud architecture, AI/ML technologies, and business transformation to create scalable, secure, and responsible AI solutions that deliver measurable business value.

Key Responsibilities

• - Design end-to-end AI, Generative AI, and ML solution architectures on GCP.
• - Define enterprise AI strategies, roadmaps, and reference architectures.
• - Architect Retrieval-Augmented Generation (RAG), Agentic AI, and AI-powered business applications.
• - Evaluate and select appropriate Foundation Models, Gemini models, and open-source LLMs.
• - Design prompt engineering, fine-tuning, grounding, and vector search strategies.
• - Design scalable, secure, and highly available cloud-native architectures using Vertex AI, BigQuery, Cloud Storage, Dataflow etc
• - Define multi-region and disaster recovery strategies.
• - Ensure architectural alignment with enterprise cloud standards.
• - Establish enterprise MLOps and LLMOps frameworks.
• - Design automated model development, deployment, monitoring, and governance pipelines.
• - Define model lifecycle management processes.
• - Implement CI/CD practices for AI solutions.
• - Monitor performance, model drift, and operational efficiency.
• - Design modern data architecture supporting AI initiatives.
• - Create data lakes, lakehouses, and enterprise analytics solutions.
• - Establish feature engineering and data preparation frameworks.
• - Define data quality, governance, lineage, and metadata management practices.

Required Experience

• - 8+ years of experience in cloud architecture, software engineering, or data platforms.
• - 3+ years of experience designing AI/ML solutions on GCP.
• - Experience leading enterprise-scale cloud transformation programs.
• - Experience architecting Generative AI and LLM-based applications.
• - Machine Learning model lifecycle
• - Natural Language Processing (NLP)
• - Computer Vision
• - Generative AI
• - Large Language Models (LLMs)
• - Vertex AI Model Garden
• - Prompt Engineering
• - Fine-tuning Foundation Models
• - Vector Databases and Embeddings
• - RAG Architecture
• - AI Agents and Multi-Agent Systems