About this role
The Architect AI Data Engineer will lead the end-to-end architecture and strategy for enterprise-scale Generative AI platforms. This role involves designing scalable agentic systems, establishing reference architectures, and driving the implementation of RAG pipelines and LLM-based solutions. You will provide technical leadership, mentor engineering teams, and act as a solution advisor to senior stakeholders and clients. Key Responsibilities: Define and lead architecture for GenAI platforms and agentic workflows; conduct technology evaluations for LLMs and vector databases; ensure engineering excellence through robust CI/CD, observability, and API development; implement governance, risk management, and Responsible AI practices including hallucination control and bias mitigation; and collaborate with data engineering and business teams to translate complex problems into AI-driven solutions. Required Qualifications: 12–15 years of total experience, with at least 3 years specifically in GenAI/LLM-based systems; strong hands-on expertise in Python/PySpark, RAG pipelines, and agent orchestration frameworks like LangChain or LangGraph; deep understanding of LLM limitations and optimization; and a solid foundation in data engineering or data science lifecycles. Preferred Qualifications: Experience with fine-tuning techniques (LoRA, PEFT), Azure AI stack, knowledge graphs, and domain-specific GenAI solutions in sectors like Insurance, BFSI, or Healthcare. The role requires a strong background in cloud platforms (Azure/AWS/GCP) and containerization technologies.