Platform Engineer

AI Platform Engineering

Platform Engineer

Position Title: Engineer, Platform Engineering

Function: Technology

Experience: 5–8 Years

Location: Bangalore, India

Reporting: Individual Contributor (No Direct Reports)

Role Summary

The Platform Engineer is responsible for owning and continuously enhancing the enterprise Data & AI Platform , including compute, storage, orchestration, semantic layer, AI services, and platform infrastructure. The role focuses on building scalable data and AI capabilities through automation, DevOps, Infrastructure as Code (IaC), governance, observability, security, and cloud optimization while enabling enterprise AI initiatives.

Key Responsibilities

• Own and continuously enhance the enterprise Data & AI Platform , including compute, storage, orchestration, semantic layer, AI services, and platform infrastructure.
• Build, automate, and maintain scalable data and AI pipelines using Infrastructure as Code (IaC), CI/CD, and DevOps best practices.
• Enable enterprise AI capabilities, including semantic models, AI agents, MCP integrations, and reusable platform services.
• Lead platform upgrades, migrations, performance optimization, and technology adoption to improve scalability, resilience, and reliability.
• Implement platform observability across data pipelines, AI services, platform health, usage, and operational SLAs.
• Establish and enforce platform security, data governance, AI governance, compliance, privacy, and auditability standards.
• Enable secure self-service access through semantic models, data products, catalogs, and role-based access controls (RBAC).
• Optimize cloud infrastructure, platform performance, operational costs, and AI usage costs.
• Collaborate with Data Engineers, AI Engineers, Data Scientists, Analytics teams, and business stakeholders to deliver enterprise Data & AI capabilities.
• Evaluate, onboard, and operationalize emerging technologies across Data, AI, Agentic AI, MCP, and analytics platforms.
• Define engineering standards, reusable frameworks, reference architectures, and technical documentation.
• Support platform operations, incident management, root cause analysis, capacity planning, and continuous service improvement.

Mandatory Technical Skills

• Strong understanding of enterprise Data & AI Platform architecture .
• Strong understanding of Semantic Layer, AI Governance, AI cost management , and enterprise AI enablement.
• Proficiency in Python (PySpark) and SQL .
• Experience building, optimizing, and monitoring batch, streaming, and AI-enabled data pipelines .
• Hands-on experience with orchestration platforms such as Apache Airflow, Astronomer , or similar workflow engines.
• Experience with cloud platforms ( AWS, Azure, or GCP ).
• Exposure to Snowflake, Databricks, Spark, Microsoft Fabric, or BigQuery .
• Understanding of data modeling, metadata management, data quality, observability, and monitoring .
• Experience with CI/CD, Infrastructure as Code (IaC), Git, and DevOps .
• Knowledge of RBAC, security, governance, privacy, compliance , and enterprise access controls.
• Good understanding of AI Services, Agentic AI, MCP, APIs , and enterprise AI integration patterns.
• Ability to monitor, troubleshoot, optimize, and support enterprise Data & AI platforms.

Good-to-Have Skills

• Power BI
• MicroStrategy
• Retail Domain Experience

Position Scope

• Hands-on experience with cloud platforms (AWS, Azure, GCP).
• Experience building enterprise data products and semantic models.
• Strong experience with batch, streaming, and event-driven pipelines.
• Experience with Airflow, Astronomer, CI/CD, Git, and IaC.
• Knowledge of Lakehouse, Data Warehouse, Semantic Layer, and enterprise data products.
• Experience implementing governance, metadata management, data quality, security (RBAC), and compliance.
• Exposure to AI Services, Agentic AI, MCP, APIs, and enterprise AI integration patterns.
• Experience delivering scalable, reliable, and cost-efficient cloud Data & AI platforms.
• Exposure to platform modernization and emerging Data & AI technologies.
• Strong collaboration with Data Engineers, AI Engineers, Data Scientists, Analytics teams, Product teams, and business stakeholders.

Key Traits

• Strong engineering mindset with automation and standardization.
• Passion for scalable, reusable enterprise platform capabilities.
• Strong understanding of enterprise data products and AI governance.
• Adaptability to evolving cloud and AI technologies.
• Excellent stakeholder management and collaboration skills.