Data Architect

LUMIQ; Dadri; On-site; INR 400,000 - 700,000; Full time

Job summary

LUMIQ is seeking a Cloud Data & AI Architect to design and implement scalable cloud data platforms for BFSI clients, covering ingestion, lakehouse, governance and AI workflows. You will guide architecture choices for underwriting, claims, onboarding and servicing, aligning with business goals and client needs.

Collaborate with data engineers, AI engineers and analysts to craft resilient, cost-efficient and secure solutions, ensuring compliance with industry standards and optimized performance.

Qualifications

• Bachelor's or master's degree in computer science, information technology, or a related field.
• Minimum 8 years in cloud architecture, design and deployment with experience in large-scale data platforms and AI workloads.
• Strong knowledge of major cloud platforms and data/AI services.
• Proficiency in SQL and Python, plus infrastructure as code (Terraform).
• Hands-on with distributed processing, lakehouse architectures and open table formats.

Responsibilities

• Design, develop, and implement scalable, secure cloud architectures for data platforms.
• Lead ingestion, CDC, lakehouse design, and transformation pipelines across enterprise systems.
• Architect agentic AI workloads and GenAI services in production environments.
• Collaborate with cross-functional teams to translate requirements into cloud solutions.
• Ensure data governance, security, and regulatory compliance in BFSI contexts.

Skills

Cloud Architecture

SQL

Python

Data Modeling

ETL

CI/CD

Kubernetes

Terraform

Lakehouse

GenAI

Education

Bachelor's or Master's in CS/IT

Tools

Docker

Kubernetes

Terraform

Snowflake

Databricks

Job description

We are seeking a knowledgeable and innovative Cloud Data & AI Architect to join our dynamic team at LUMIQ. Data is the core of what we do. The ideal candidate will design and implement large-scale cloud data platforms for our BFSI clients — Ingestion and CDC, Lakehouse and Open Table formats, Transformation, Governance and serving — and then extend that foundation into production agentic AI systems that run underwriting, claims, onboarding and servicing workflows. You will collaborate with cross-functional teams, assess the technological landscape, and create architecture that aligns with our business objectives and client needs.

Key Responsibilities:

Cloud Solution Design and Development:

• Design, develop, and implement scalable, secure, and flexible cloud architectures.


• Translate business requirements into cloud solutions, ensuring alignment with organizational objectives.


• Lead the deployment of applications, data platforms and AI workloads in cloud environments.


• Good knowledge of at least two of (w.r.t data projects) – Azure, AWS, GCP, Snowflake, Databricks

Data Platform Architecture and Engineering:

• Architect ingestion at enterprise scale — batch, change data capture and near-real-time streaming from core banking, policy administration, claims and third-party systems.


• Design the lakehouse — open table formats, layered storage (raw, cleansed, curated, consumption), schema evolution, partitioning and compaction.


• Own transformation and orchestration design — modelled, tested and version-controlled pipelines rather than hand‑written one‑offs.


• Choose storage, compute and serving options against latency, concurrency and cost, and keep optimizing — cost is a standing client conversation.


• Data governance, cataloguing, lineage, data quality and observability, with fine‑grained access control, PII handling and masking.


• Build reusable industry data models and accelerators that shorten every subsequent implementation.

Agentic AI and GenAI Architecture:

• Architect production agentic systems with the orchestration layer decoupled from the inference layer, deployed on containers inside the customer's private network.


• Design the data and context layer agents depend on: retrieval over governed sources, feature and context engineering, and document AI / IDP pipelines.


• Define the boundary between agent autonomy and deterministic control — scoped tools, confirmation gates, idempotency and human‑in‑the‑loop.


• Handle long‑running workflows properly — durable agent state, checkpointing, resumability and message‑history management across multi‑step runs.


• Build explainability, auditability and evaluation in from day one: immutable request and response trails, model versioning and rollback, groundedness and drift measurement.


• Good at deriving BOM (Bill of Material) and effort estimate with minimal information, for both data and agentic workloads.


• Conduct and lead POCs, customer demos, and architecture and security reviews.


• Partner with cloud and platform vendors on co‑sell, funding programmes and joint solution reviews.

Technology Evaluation and Adoption:

• Evaluate emerging cloud, data and AI technologies, services, and solutions.


• Recommend adoption of new technologies and best practices, and build reusable accelerators that shorten delivery.


• Stay informed on industry trends in cloud, data engineering, agentic AI and the BFSI sector.

Collaboration and Stakeholder Engagement:

• Work closely with data engineers, data scientists, AI engineers, analysts and client IT teams to understand their needs and challenges.


• Communicate complex data and AI solutions effectively to both technical and non‑technical audiences.


• Engage in cross‑functional projects and initiatives to ensure architecture supports and advances business objectives.

Security and Compliance:

• Implement and maintain cloud, data and AI security best practices to safeguard sensitive data.


• Ensure compliance with BFSI regulations and standards — data residency, RBI/IRDAI guidelines, PII handling and auditability.


• Collaborate with security teams on regular assessments, and on AI‑specific risks such as prompt injection, malicious documents, data leakage and model governance.

Performance Optimization:

• Monitor and optimize data pipelines, query workloads and inference infrastructure for performance, cost, and scalability.


• Troubleshoot and resolve issues across cloud environments, data pipelines and agent runtimes.


• Provide technical guidance and support to development teams.

Skills & Qualification

• Bachelor's or master's degree in computer science, Information Technology, or a related field.


• Minimum of 8 years in cloud architecture, design and deployment, with real depth in large‑scale data platforms and recent hands‑on exposure to AI/GenAI workloads.


• Strong knowledge of major cloud platforms and their managed data and AI services.


• Strong SQL and Python, and infrastructure as code (e.g., Terraform).


• Hands‑on with distributed processing, modern lakehouse and open table formats, and workflow orchestration.


• Working knowledge of containerization and orchestration (e.g., Docker, Kubernetes).


• Familiarity with LLM application patterns — retrieval, tool / function calling, agent orchestration, durable state, and evaluation.


• Understanding of BFSI industry compliance, regulations, and security requirements.


• Excellent communication, collaboration, and problem‑solving skills.

Nice to Have:

• Cloud architect certification from a major provider, plus a data engineering, machine learning or GenAI speciality.


• Experience with DevOps practices and CI/CD pipelines.


• Experience taking a data platform or an agentic system into production in a regulated environment.


• Open‑source contributions to data or agent tooling.


• Knowledge of BI and semantic‑layer tooling, vector stores and LLM observability.