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Lumiq.ai

Posted 5 Nov 2026

Data and AI Architect

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Employment
Full time
Team
Software Engineering
Posted
5 Nov 2026

About the role

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.

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