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Senior niche GenAI architect reduces qualified applicant density despite market demand.
Strong specialization in GenAI, LLMs, RAG and data engineering limits cross-industry transferability.
Explicit 12–15 years and mandatory GenAI, architecture and tech stack requirements create strict filters.
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Lead the end-to-end architecture and technical decision-making for enterprise GenAI platforms and scalable agentic AI systems.
Design, implement, and optimize complex agentic workflows and RAG pipelines focusing on retrieval accuracy, latency, cost, scalability, and reliability.
Provide technical leadership including platform development, standards setting, governance for responsible AI, stakeholder advisement, and cross-team collaboration.
126 years total work experience with at least 3 years in GenAI/LLM-based systems.
Proven experience leading architecture and delivery of enterprise AI/GenAI solutions.
Strong hands-on expertise with LLMs (Claude, OpenAI), RAG pipelines, agent orchestration frameworks (LangChain, LangGraph), and GPT + Agentic AI implementations.
Proficiency in Python/Pyspark, API integration, cloud platforms (Azure/AWS/GCP), data engineering (Fabric/Azure Databricks/Snowflake), containers (Docker/Kubernetes), CI/CD pipelines, and data/AI foundational skills such as NLP, data engineering or ML lifecycle.
Senior-level AI architect with deep expertise in agentic AI system design and GenAI platform architecture in enterprise environments.
Experienced in leading multidisciplinary teams to implement scalable, production-grade AI workflows integrating with data platforms and cloud infrastructure.
Strategic thinker who can align complex AI technical solutions with business needs and governance requirements, and effectively communicate with senior leadership and stakeholders.