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Senior, niche GenAI architect role at a non-Tier1 firm with metro location reduces applicant density.
Requires deep GenAI, LLMOps and enterprise data engineering expertise, limiting cross-industry transferability.
Explicit 15+ years, mandatory GenAI/LLM architecture and data engineering requirements create strict shortlisting filters.
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Lead design and implementation of enterprise-scale GenAI and agentic architectures including end-to-end RAG pipelines ensuring scalability, reliability, and cost optimization.
Provide technical leadership on prompt engineering, multi-agent workflows, hallucination mitigation, and develop production-grade APIs and MLOps/LLMOps pipelines.
Build and scale GenAI CoE, drive organizational adoption of best practices, engage CXOs and stakeholders for AI solutioning, and establish governance and compliance policies.
15+ years total experience in Data Engineering, Data Science, or AI.
3+ years hands-on experience in LLM/GenAI solutions at scale with proven architecture and enterprise delivery expertise.
Strong engineering skills in Python/Pyspark, API integration, and data engineering platforms like Fabric, Azure Databricks, Snowflake; exposure to cloud (Azure/AWS/GCP).
Prior experience with data engineering (ETL/ELT, pipelines) or ML lifecycle (especially NLP).
Experienced in architecting complex GenAI and multi-agent AI systems with focus on solution scalability, reliability, and cost efficiency.
Skilled in leading cross-functional teams and engaging senior stakeholders including CXOs for AI strategy and client-facing AI solution design.
Deep knowledge of LLM limitations, evaluation, optimization, and GenAI governance including security, privacy, and ethical compliance frameworks.