Data and AI Architect

We are looking for an experienced and innovative Cloud Data and AI Architect to design and implement large-scale, secure, and scalable cloud data platforms for BFSI clients. The ideal candidate should have strong expertise in cloud architecture, data engineering, lakehouse architecture, and hands-on exposure to AI/GenAI workloads.

The candidate will be responsible for architecting enterprise-scale data platforms, designing modern data pipelines, deploying production-grade Agentic AI solutions, and providing technical leadership across cloud and AI initiatives.

Responsibilities:

• Design and implement scalable, secure, and flexible cloud architectures.
• Translate business requirements into effective cloud solutions.
• Lead the deployment of applications, data platforms, and AI workloads.
• Demonstrate strong knowledge of at least two technologies among AWS, Azure, GCP, Snowflake, and Databricks.
• Architect enterprise-scale data ingestion using batch processing, CDC, and near-real-time streaming.
• Design Lakehouse architectures using open table formats and layered storage.
• Lead data transformation, orchestration, and pipeline development.
• Optimize storage, compute, performance, scalability, and cloud costs.
• Implement data governance, cataloguing, lineage, data quality, and observability.
• Develop reusable code, accelerators, and solutions for faster implementation.
• Architect and deploy production-grade Agentic AI and GenAI systems.
• Work with LLMs, RAG, agent orchestration, and tool/function calling.
• Design secure data and context layers for AI agents.
• Implement human-in-the-loop controls, checkpoints, and durable agent workflows.
• Ensure AI systems have proper monitoring, explainability, auditability, and evaluation.
• Experience deploying AI-generated code into production and maintaining it.
• Prepare Bill of Materials (BOM) and effort estimations for data and AI projects.
• Lead Proof of Concepts (POCs), customer demonstrations, and technical discussions.
• Participate in architecture and security reviews.
• Communicate technical solutions effectively to clients and stakeholders.
• Implement cloud, data, and AI security best practices.
• Ensure data privacy, PII handling, masking, and governance.
• Understand BFSI compliance requirements, including RBI/IRDAI guidelines.
• Optimize data pipelines, query workloads, and AI inference infrastructure.
• Troubleshoot cloud, data engineering, and AI runtime issues.

Requirements:

• Bachelor's or master's degree in computer science, information technology, or a related field.
• Minimum 8 years of experience in cloud architecture, cloud data engineering, and large-scale data platform design.
• Hands-on exposure to AI/GenAI workloads.
• Strong understanding of major cloud platforms and their managed data and AI services.
• Experience with modern Lakehouse architectures and distributed processing.
• Excellent communication, stakeholder management, and problem-solving skills.

Required Technical Skills:

• Cloud Platforms: AWS, Microsoft Azure, Google Cloud Platform (GCP).
• Data Platforms: Snowflake, Databricks.
• Programming: Strong SQL and Python.
• Data Engineering: Apache Spark, CDC, batch processing, real-time streaming.
• Data Architecture: Lakehouse, open table formats, data modeling, and data pipelines.
• AI/GenAI: LLMs, RAG, Agentic AI, Agent Orchestration, Document AI.
• Infrastructure: Terraform, Docker, Kubernetes.
• Data Governance: Data quality, lineage, cataloging, PII handling, and data masking.
• DevOps: CI/CD and workflow orchestration.

Good to Have:

• Cloud architecture certifications from AWS, Azure, or GCP.
• BFSI domain experience, particularly banking, insurance, NBFCs, or financial services.
• Experience deploying data platforms or Agentic AI systems in regulated environments.
• Knowledge of vector databases, BI tools, semantic layers, and LLM observability.
• Open-source contributions to data or AI tooling.

Skills

AWS, Architect, Architecture, Azure, Databricks, GCP, Python, RAG, SQL, Snowflake, Spark, Terraform