Job Description
Locations: Los Angeles, CA / Dallas, TX / Chicago, IL
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Must Have Technical/Functional Skills
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• Architect enterprise data platforms for data lake, Lakehouse, streaming systems.
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• Design data integration and data pipeline patterns
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• Should be able to evaluate new technologies and run proof of concepts.
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• Should be able to set data and AI strategy for data organization.
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• Established data Quality, lineage and metadata standards
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• Ensured compliance with privacy, security and regulation
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• Drives adoption of responsible AI frameworks
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• Created architectural guardrails
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• Drive consensus on standards (eg data contracts, lineage) across different data organizations
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• Reviews design and elevate architectural thinking across teams
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• Creates reusable patterns, templates and reference architectures
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• Very strong understanding and experience on Data products, data mesh and Medallion Architecture implementation
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• Design and implement AI and Gen AI solution for data value chain
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• Strong experience with LLMs, prompt engineering, and agent frameworks (LangChain, AutoGen, CrewAI).
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• Deep understanding of MCPs, ReAct, Tree of Thought, and AutoGPT-style reasoning.
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• Hands-on with Python, OpenAI APIs, Anthropic Claude, Vector DBs (FAISS, Pinecone, Weaviate).
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• Experience with A2A orchestration, agent memory strategies, and tool calling.
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• Strong grasp of enterprise architecture, data governance, and security protocols.
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• Experience with cloud platforms (Azure, AWS, GCP) and MLOps pipelines.
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• Very strong understanding and experience on Data products, data mesh and Medallion Architecture implementation
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• Implement retrieval-augmented generation (RAG) pipelines with memory, context management, and tool usage.
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• Define Model Context Protocol (MCPs) to chain reasoning, retrieval, and action models.
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• Hands-on with Python, OpenAI APIs, Anthropic Claude, Vector DBs (FAISS, Pinecone, Weaviate).
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Roles & Responsibilities
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• Lead enterprise data platform modernization from on-prem to cloud for banking and insurance clients.
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• Architect and implement scalable solutions using Snowflake and Databricks on AWS and Azure
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Design and implement AI and Gen AI solution for data value chain
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• Design data integration pipelines (batch, real-time, big data) and analytics platforms
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• Define and implement data governance, quality, meta data, and lineage frameworks and should be able to leverage GenAI capabilities.
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• Act as a trusted advisor to senior business and IT stakeholders
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Architect Agentic AI ecosystems using LLMs, vector databases, and orchestration frameworks (LangChain, AutoGen, CrewAI).
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• Define Model Context Protocol (MCPs) to chain reasoning, retrieval, and action models.
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• Design Agent-to-Agent (A2A) communication protocols for collaborative multi-agent workflows.
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• Implement retrieval-augmented generation (RAG) pipelines with memory, context management, and tool usage.
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Generic Managerial Skills, If any
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• 15–20 years of experience in data architecture, data engineering, and analytics platforms
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• Strong consulting experience in large BFSI transformation programs
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• Hands-on expertise with Snowflake and Databricks (Lakehouse architecture)
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• Design and implement AI and Gen AI solution for data value chain
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• Very strong understanding and experience on Data products, data mesh and Medallion Architecture implementation
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• Experience with cloud data services in aws,azure,gcp
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• Strong background in data integration, reporting, and big data ecosystems
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• Experience working in regulated environments with data governance and compliance requirements
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• Excellent stakeholder communication and leadership skills