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Tier-1 brand and metro locations increase competition, but senior specialized AI requirements moderate density.
Deep LLM/MLOps architecture skills required but transferable across AI-mature industries, so moderate sensitivity.
Explicit 15+ years, mandatory hands-on Python and AI production experience, and governance requirements make filters strict.
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Own end-to-end architecture for enterprise-scale GenAI and AI-powered solutions within Self-Service Platform (SSP), focusing on scalability, security, and production readiness.
Design and implement agentic AI applications and RAG solutions, including foundation model integrations, prompt strategies, and performance/cost optimization.
Lead AI platform lifecycle guidance, embed Responsible AI governance, define GenAI reference architectures, and oversee backend API and cloud-native deployments.
Bachelor’s or Master’s degree in Computer Science/Engineering or equivalent from recognized institutions (verification applies).
15+ years of software architecture experience with minimum 3 years designing AI/ML or LLM-based production systems.
Mandatory hands-on expertise in Python (FastAPI, asyncio), ML/DL frameworks (PyTorch, TensorFlow), cloud-native and distributed services.
Experience with RAG pipelines, embeddings, vector databases and agentic GenAI frameworks (LangChain, LangGraph) and cloud AI services (Azure OpenAI, AWS Bedrock).
Experienced in designing complex AI/ML architectures with a focus on agentic GenAI and responsible AI governance for enterprise-grade deployment.
Strong operational leadership in full AI model lifecycle including MLOps/LLMOps, observability, security, and cost/performance optimizations.
Comfortable working in cloud-native environments using Docker, Kubernetes, Terraform and deploying scalable backend APIs for AI platforms.