Senior Cloud Data & AI Architect

About the Role

Lead enterprise data platform modernization from on-prem to cloud for banking and insurance clients. Act as a trusted advisor to senior business and IT stakeholders.

Responsibilities

• Architect enterprise data platforms for data lake, Lakehouse, and streaming systems.
• Design data integration and data pipeline patterns (batch, real-time, big data) and analytics platforms.
• Evaluate new technologies and run proof-of-concepts.
• Set data and AI strategy for the data organization.
• Establish data quality, lineage, and metadata standards; ensure compliance with privacy, security, and regulation.
• Drive adoption of responsible AI frameworks and create architectural guardrails.
• Drive consensus on standards (e.g., data contracts, lineage) across different data organizations.
• Review designs and elevate architectural thinking across teams.
• Create reusable patterns, templates, and reference architectures.
• Design and implement AI and Gen AI solutions for the data value chain, including retrieval-augmented generation (RAG) pipelines with memory, context management, and tool usage.
• Architect Agentic AI ecosystems using LLMs, vector databases, and orchestration frameworks (LangChain, AutoGen, CrewAI).
• Define Model Context Protocol (MCPs) to chain reasoning, retrieval, and action models.
• Design Agent-to-Agent (A2A) communication protocols for collaborative multi-agent workflows.
• Implement data governance, quality, metadata, and lineage frameworks, leveraging GenAI capabilities.
• Architect and implement scalable solutions using Snowflake and Databricks on AWS and Azure.

Requirements

• 15–20 years of experience in data architecture, data engineering, and analytics platforms.
• Strong consulting experience in large BFSI transformation programs.
• Hands-on expertise with Snowflake and Databricks (Lakehouse architecture).
• Very strong understanding and experience with data products, data mesh, and Medallion Architecture implementation.
• Experience with cloud data services in AWS, Azure, and GCP.
• Strong background in data integration, reporting, and big data ecosystems.
• Experience working in regulated environments with data governance and compliance requirements.
• Excellent stakeholder communication and leadership skills.

Skills

• Deep understanding of LLMs, prompt engineering, and agent frameworks (LangChain, AutoGen, CrewAI).
• Strong grasp of MCPs, ReAct, Tree of Thought, and AutoGPT-style reasoning.
• Hands-on with Python, OpenAI APIs, Anthropic Claude, and vector databases (FAISS, Pinecone, Weaviate).
• Experience with A2A orchestration, agent memory strategies, and tool calling.
• Strong grasp of enterprise architecture, data governance, and security protocols.
• Experience with MLOps pipelines.

Pay

$150,000–$175,000 a year