Description

Architect enterprise data platforms and AI solutions for banking and insurance clients, modernizing on-prem systems to cloud-native Lakehouse and streaming architectures.

This role is hybrid.

Responsibilities

• Design scalable data integration pipelines (batch, real-time) and analytics platforms using Snowflake and Databricks on AWS and Azure.
• Architect enterprise data platforms including data lakes, Lakehouses, and streaming systems while establishing data quality, lineage, and metadata standards.
• Design and implement Agentic AI ecosystems, RAG pipelines, and GenAI solutions using LLMs, vector databases, and orchestration frameworks.
• Define Model Context Protocols (MCPs) and Agent-to-Agent (A2A) communication for multi-agent workflows and tool usage.
• Drive data governance, security, and responsible AI frameworks while elevating architectural thinking across teams.

Required Skills

• 15+ years in data architecture, engineering, and analytics, with strong consulting experience in BFSI transformations.
• Deep expertise in Snowflake and Databricks (Lakehouse architecture) on AWS, Azure, or GCP.
• Hands-on experience with LLMs, prompt engineering, and agent frameworks (LangChain, AutoGen, CrewAI).
• Proficiency in Python, OpenAI APIs, Anthropic Claude, and Vector DBs (FAISS, Pinecone, Weaviate).
• Strong understanding of Data Mesh, Medallion Architecture, and data product implementation.
• Experience implementing RAG pipelines with memory, context management, and tool calling.
• Knowledge of MLOps pipelines, enterprise architecture, and regulated environment compliance.

Preferred Skills

• Deep understanding of MCPs, ReAct, Tree of Thought, and AutoGPT-style reasoning.
• Experience with A2A orchestration and agent memory strategies.

Key Skills

Education

Any Graduate