Generative AI Architect

About the Role:

• Design end-to-end GenAI architectures integrating agentic systems, retrieval frameworks, vector databases, and feedback pipelines for continuous learning.
• Define multi-cloud and on-prem GenAI platforms ensuring scalability, interoperability, and resilience.
• Architect data pipelines, feature stores, and real-time ingestion systems with strong governance and security.
• Implement responsible AI principles covering data privacy, model transparency, and compliance.
• Establish CI/CD, MLOps, and GenAIOps pipelines for automated deployment, prompt management, and model evaluation.
• Optimize LLMs, frameworks, and compute infrastructure for performance, cost, and reliability.
• Collaborate with business, product, and engineering teams to deliver domain-specific GenAI solutions such as data cleaning strategies, automated validation and testing, intelligent log analysis, and refinement using feedback loop.

Requirements:

• 12+ years in software architecture, data engineering, or solution design, including 3+ years in GenAI/LLM systems.
• Proven expertise in LLM orchestration, agentic frameworks, and graph-based RAG architectures.
• Strong grasp of data architecture, streaming pipelines, and vector database integration.
• Experience across AWS/Azure/GCP and on-prem environments, including Kubernetes and GPU clusters.
• Solid understanding of authentication, RBAC, IAM, and secure integration patterns.
• Good understanding of CI/CD, MLOps, and GenAIOps.
• Excellent documentation and presentation skills.
• Nice to have experience in networking, or large-scale testing domains with prior involvement in enterprise AI or digital transformation initiatives.