Roles and Responsibilities

1. Platform Architecture & Design

Design the end-to-end Azure Data & AI platform using Azure Databricks Lakehouse, ADLS Gen2, and related Azure services.

Define reference architectures and engineering standards for data engineering, ML, and GenAI workloads.

Drive secure, scalable, and cost-efficient architecture decisions across compute, storage, networking, identity, and environment design.

1. Data Platform & Engineering Leadership

Lead a small team of data/platform engineers through planning, code reviews, mentoring, and delivery governance.

Design and oversee scalable batch and near real-time ingestion and transformation pipelines using Databricks, PySpark, Delta Lake, APIs, and event-driven integrations.

Enable trusted, AI-ready curated datasets with clear quality controls, SLAs, and data contracts.

3. AI/GenAI Platform Enablement

Enable platform patterns for Azure OpenAI, RAG pipelines, embeddings lifecycle management, and vector search.

Support ML and LLM use cases by providing feature-ready data pipelines, inference-ready integration patterns, and reusable platform components.

Collaborate with Data Science and AI/ML teams to productionize models and GenAI solutions using Azure ML and Databricks-native capabilities where relevant.

1. Governance, Security & Environment Management

Implement governance-by-design across RBAC, Key Vault, private connectivity, data lineage, cataloging, auditability, and controlled access.

Ensure clear Dev, UAT, and Prod environment separation, release controls, and alignment with enterprise architecture and security standards.

Work closely with cloud, security, and governance teams to ensure compliance with Jio-bp and RIL guardrails.

1. DevOps, MLOps & Platform Reliability

Define and implement CI/CD, infrastructure-as-code, release automation, and platform observability standards for data and AI workloads.

Be hands-on in setting up and reviewing YAML pipelines, deployment workflows, automated testing, monitoring, and runbooks as needed.





Enable practical MLOps/LLMOps practices including versioning, deployment pipelines, model promotion, monitoring, and incident readiness.

Partner with platform/cloud engineering teams for production deployment patterns, including containerized model serving and AKS-based workloads where required.

1. Performance Optimization & FinOps

Drive cost optimization, workload tuning, and capacity planning across Databricks and Azure services.

Implement platform controls such as cluster policies, autoscaling, storage optimization, and usage guardrails.

1. Stakeholder Management & Technical Leadership

Lead architecture reviews, design decisions, and technical solution validation across cross-functional teams.

Work closely with business, data, AI/ML, security, and digital teams to align platform delivery with business priorities.

Provide strong technical leadership while remaining close enough to the implementation to unblock teams and accelerate delivery.

- Job Requirements :

1. B.Tech. in Computer Science / IT / Engineering or equivalent.
2. 8–12 years of experience across data engineering, cloud platform architecture, and modern data platform delivery.
3. Strong hands-on expertise in Python, SQL, PySpark/Spark, data modeling, and lakehouse architecture.
4. Strong working experience with Azure Databricks, Delta Lake, ADLS Gen2, and enterprise data platform design on Azure.
5. Experience building batch and streaming data pipelines using tools such as Databricks workflows, Lakeflow, APIs, Event Hub, or Kafka.
6. Positive experience with Azure DevOps and/or GitHub Actions, infrastructure as code (Terraform/Bicep/ARM), and environment release management.
7. Experience with platform security patterns such as RBAC, Key Vault, managed identity, private endpoints, and controlled access.
8. Understanding of data governance, lineage, and cataloging using Purview, Unity Catalog, or equivalent tools.
9. Practical exposure to Azure ML, Azure OpenAI, LLM APIs, embeddings, RAG patterns, and vector search.
10. Ability to translate architecture into implementation standards and guide teams through production delivery.

-

📌 Cloud Architect Lead (Mumbai)
🏢 Jio bp
📍 Mumbai