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Senior, highly specialized GenAI leadership reduces candidate density despite general market interest.
Highly specialized GenAI/LLM platform expertise limits transferability across unrelated industries.
Explicit 15+ years and mandatory LLM/architecture skills create very strict shortlisting filters.
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Lead architecture design and implementation of enterprise-grade GenAI and agentic AI solutions including end-to-end RAG pipelines.
Define organisational reference architectures, reusable frameworks, and best practices to drive scalability, reliability, and cost optimization of GenAI platforms.
Drive CoE development, stakeholder engagement with CXOs, strategic AI roadmap decisions, and technical mentorship for architects, engineers, and data scientists.
15+ years total experience in Data Engineering, Data Science, or AI.
3+ years hands-on experience with LLM/GenAI solutions at scale including architecture and enterprise delivery.
Strong proficiency in Python/Pyspark engineering, API integration, and experience with cloud platforms (Azure/AWS/GCP).
Prior experience with Data Engineering (ETL/ELT), ML lifecycle (especially NLP), or analytics engineering mandatory.
Experienced in leading large-scale GenAI/agentic AI architecture with end-to-end ownership of solution design and deployment.
Familiar with LangChain, LangGraph or similar frameworks, prompt engineering, agent orchestration, and LLM limitations optimization.
Skilled at collaborating with senior stakeholders (CXOs, clients) and driving organisational AI strategy, governance, and capability building initiatives.