Senior Cloud Data & AI Architect

Job Title: Senior Cloud Data & AI Architect

Experience: 10–18 Years
Employment Type: Full Time
Job Type: Permanent / Direct Hire

Location: NJ/NYC/Dallas/Charlotte/Atlanta/Chicago

Job Summary

We are seeking a highly experienced Senior Cloud Data & AI Architect to design and lead enterprise-scale cloud data platforms, data modernization, AI/GenAI solutions, and Agentic AI architectures.

The ideal candidate will have strong expertise in Data Architecture, Cloud Data Platforms, Snowflake, Databricks, Data Lakehouse, Data Mesh, Medallion Architecture, Data Governance, Generative AI, LLMs, RAG, and Agentic AI.

This role will provide technical leadership for enterprise data and AI transformation initiatives, including cloud migration, data platform modernization, data governance, AI strategy, and development of reusable architecture patterns.

Required Qualifications

• 10–18 years of experience in Data Architecture, Data Engineering, Cloud Data Platforms, Analytics, or Technology Architecture.

• Strong experience architecting enterprise Data Lakes, Lakehouses, streaming platforms, and analytics ecosystems.

• Hands-on experience with Snowflake and Databricks, including Lakehouse architecture.

• Strong understanding of Data Products, Data Mesh, and Medallion Architecture.

• Experience designing batch, real-time, streaming, and big data integration pipelines.

• Strong experience with data governance, data quality, metadata management, data lineage, privacy, security, and regulatory compliance.

• Experience developing enterprise AI and Generative AI solutions across the data value chain.

• Strong hands-on experience with LLMs, Prompt Engineering, RAG, Vector Databases, and Agentic AI frameworks.

• Experience with LangChain, AutoGen, and/or CrewAI.

• Strong understanding of Model Context Protocol (MCP), ReAct, Tree-of-Thought, tool calling, and agent orchestration.

• Hands-on experience with Python, OpenAI APIs, Anthropic Claude, and vector databases.

• Experience with AWS, Azure, and/or Google Cloud Platform cloud platforms.

• Experience designing and implementing MLOps / AI deployment pipelines.

• Strong understanding of enterprise architecture, data security, governance, and responsible AI.

• Excellent consulting, communication, stakeholder management, and technical leadership skills.

Key Responsibilities

<>Cloud Data Architecture

• Architect enterprise-scale cloud data platforms supporting data lakes, Lakehouses, streaming, analytics, and AI workloads.

• Lead data platform modernization and migration initiatives from on-premises environments to AWS and Azure cloud platforms.

• Design scalable data architectures using Snowflake and Databricks.

• Develop enterprise architecture patterns for data ingestion, integration, transformation, processing, storage, and analytics.

• Design data pipelines supporting batch, real-time, streaming, and big data workloads.

• Create reusable reference architectures, design patterns, templates, standards, and architectural guardrails.

• Evaluate emerging data and AI technologies and lead Proof of Concept (PoC) initiatives.

<>Data Architecture & Governance

• Define and implement enterprise data governance, data quality, metadata, lineage, privacy, and security frameworks.

• Establish standards for data contracts, data products, metadata, lineage, and data quality.

• Drive adoption of Data Mesh and Medallion Architecture principles.

• Develop and promote reusable data products and domain-oriented data architecture.

• Ensure data platforms meet organizational security, privacy, compliance, and regulatory requirements.

• Establish architectural guardrails and standards across multiple data organizations.

• Review solution designs and elevate architectural standards across engineering and data teams.

<>AI & Generative AI Architecture

• Define enterprise Data and AI strategy aligned with business and technology objectives.

• Design and implement AI and Generative AI solutions across the data value chain.

• Architect enterprise LLM and Agentic AI ecosystems using LLMs, vector databases, APIs, and orchestration frameworks.

• Design and implement Retrieval-Augmented Generation (RAG) solutions with context management, memory, retrieval, and tool usage.

• Develop AI solutions using OpenAI APIs, Anthropic Claude, and other enterprise LLM platforms.

• Implement AI-powered capabilities for data discovery, data quality, metadata, governance, analytics, and data engineering.

• Define and implement Model Context Protocol (MCP) patterns for connecting reasoning, retrieval, tools, and action models.

• Design Agent-to-Agent (A2A) communication and orchestration patterns for collaborative multi-agent workflows.

• Define agent memory strategies, context management, tool calling, and multi-agent orchestration.

• Drive adoption of Responsible AI, AI governance, AI security, and model risk management practices.

Agentic AI / GenAI Technical Skills

• Large Language Models (LLMs)

• Generative AI / GenAI

• Agentic AI

• AI Agents / Autonomous Agents

• LangChain

• AutoGen

• CrewAI

• RAG / Retrieval-Augmented Generation

• Model Context Protocol (MCP)

• Agent-to-Agent (A2A)

• ReAct

• Tree-of-Thought / ToT

• Tool Calling / Function Calling

• Agent Orchestration

• Agent Memory

• Context Management

• Prompt Engineering

• OpenAI APIs

• Anthropic Claude

• Vector Databases

• FAISS

• Pinecone

• Weaviate

• Python

Cloud & Data Platform Skills

Cloud Platforms:

• AWS

• Microsoft Azure

• Google Cloud Platform (Google Cloud Platform)

Data Platforms:

• Snowflake

• Databricks

• Delta Lake

• Data Lake

• Data Lakehouse

• Data Warehouse

• Big Data

• Streaming Data Platforms

Architecture:

• Data Mesh

• Data Products

• Medallion Architecture

• Enterprise Data Architecture

• Cloud Data Architecture

• Data Platform Modernization

• Data Integration

• Data Engineering

Data Governance & Security

• Data Governance

• Data Quality

• Data Lineage

• Metadata Management

• Data Catalog

• Data Contracts

• Master Data Management

• Data Privacy

• Data Security

• Regulatory Compliance

• Responsible AI

• AI Governance

• AI Security

• Model Governance

• Enterprise Security Architecture

MLOps & Engineering

• MLOps

• AI/ML Pipelines

• CI/CD

• DevOps

• Infrastructure as Code

• API Integration

• REST APIs

• Python

• Cloud-Native Architecture

• Automated Data Pipelines

• AI Model Deployment

• Model Monitoring

Preferred Qualifications

• Experience with large-scale BFSI / Banking / Financial Services / Insurance transformation programs.

• Strong consulting background in enterprise data and AI transformation.

• Experience working with highly regulated environments and complex data governance requirements.

• Experience establishing enterprise Data & AI Centers of Excellence (CoE).

• Experience defining enterprise AI operating models and AI strategies.

• Experience leading cross-functional architecture and engineering teams.

• Strong executive stakeholder engagement and presentation skills.

Leadership Responsibilities

• Act as a trusted advisor to senior business and technology stakeholders.

• Drive architectural consensus across distributed data and technology organizations.

• Lead architecture reviews and establish enterprise technical standards.

• Mentor architects, engineers, and technical teams.

• Communicate complex data, cloud, AI, and architecture concepts to both technical and executive audiences.

• Provide technical leadership across large-scale data and AI transformation programs.

Core Dice Search Keywords

Senior Cloud Data Architect, Cloud Data Architect, Data Architect, AI Architect, Data & AI Architect, Cloud AI Architect, Enterprise Data Architect, Cloud Architect, Data Engineering Architect, AWS Data Architect, Azure Data Architect, Google Cloud Platform Data Architect, Snowflake Architect, Databricks Architect, Snowflake, Databricks, Lakehouse, Data Lake, Data Mesh, Data Products, Medallion Architecture, Data Governance, Data Quality, Data Lineage, Metadata, Data Contracts, Data Security, Data Privacy, Big Data, Streaming, Data Integration, Data Pipelines, Cloud Data Platform, Data Modernization, AI Architecture, Generative AI, GenAI, LLM, Large Language Models, Agentic AI, AI Agents, RAG, Retrieval Augmented Generation, LangChain, AutoGen, CrewAI, MCP, Model Context Protocol, A2A, Agent-to-Agent, ReAct, Tree of Thought, Prompt Engineering, OpenAI, OpenAI API, Anthropic, Claude, Vector Database, FAISS, Pinecone, Weaviate, Python, MLOps, Responsible AI, AI Governance, AI Security, AWS, Azure, Google Cloud Platform, Cloud Migration, Enterprise Architecture, BFSI, Banking, Financial Services, Insurance.

Education

Bachelor's or Master's degree in Computer Science, Information Technology, Data Science, Engineering, or a related technical field preferred.

Employment Type

Full Time / Permanent