Role & responsibilities
Define end-to-end enterprise AI architecture for scalable, secure, and responsible GenAI products leveraging LLMs, RAG, multi-agent systems, and knowledge-driven pipelines.
Architect enterprise-grade AI platforms spanning data, model, agent orchestration, APIs, and product integration across cloud-native ecosystems (Azure/AWS).
Design advanced retrieval ecosystems (vector DBs, knowledge graphs, hybrid search) to maximize contextual accuracy and minimize hallucinations.
Lead development of production-ready AI products with robust MLOps, observability, governance, evaluation frameworks, and continuous feedback loops.
Establish AI reference architectures, reusable frameworks, and engineering standards for product teams to accelerate delivery at scale.
Drive integration of structured domain knowledge (ontologies, taxonomies,



semantic layers) into AI solutions for domain-specific intelligence.
Own AI platform strategy including model selection (open/closed), fine-tuning, prompt engineering, cost optimization, and performance benchmarking.
Ensure Responsible AI practices covering security, compliance, explainability, risk mitigation, and data privacy for enterprise deployments.
Collaborate with product, engineering, business, and leadership stakeholders to translate customer pain points into scalable AI-led solutions.
Mentor teams and drive AI adoption, innovation roadmap, and enterprise-wide transformation towards AI-first product engineering.

📌 Artificial Intelligence Architect Chennai (India)
🏢 Sunovaa Tech
📍 India