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)
Data Platform Engg:
Primary skills - Full stack (React, NodeJs, CI/CD, etc.)
Secondary skills - Data engineering (including some exposure to Snowflake, Databricks or similar data platform), AI/Agentic
The resources are expected to build service layer that could be used by Data Engineers
Role Summary
Client is seeking a highly experienced Engineer, Data Platform to design, build, and operate enterprise-grade platform capabilities supporting Data, Analytics, AI, and Agentic AI workloads. The role will be instrumental in modernizing the organization's Data & AI Platform by enabling scalable, governed, self-service, and AI-ready data capabilities across the enterprise
The successful candidate will contribute to the development of Data Engineering as a Service, Self-Service Analytics, Semantic Layer capabilities, Intelligent Data Products, and Agentic AI enablement. This is a hands-on engineering role requiring strong expertise in AWS cloud technologies, Full Stack Development, Platform Engineering, Microservices Architecture, and modern software development practices.
Key Responsibilities
Design, develop, and enhance enterprise Data & AI Platform capabilities supporting analytics, AI, Agentic AI, and modern data platform use cases.
Build platform capabilities that enable Data Engineering as a Service, Data Quality as a Service, data discovery, governance, and standardized data consumption patterns
Develop semantic layer and self-service analytics capabilities to provide business users, analysts, and AI applications with trusted and governed data access
Build cloud-native applications, APIs, platform services, automation frameworks, and developer tools to improve productivity across Data Engineering, Analytics, AI, and business teams
Design and implement reusable platform services, accelerators, SDKs, and engineering frameworks for data ingestion, transformation, orchestration, observability, and platform operations
Develop secure, scalable, and resilient platform solutions on AWS using microservices, CI/CD, Infrastructure as Code (IaC), automation, and DevOps practices.
Support data quality, metadata management, lineage, observability, governance, security, privacy, and compliance initiatives
Enable AI-ready platform architecture through data, metadata, orchestration, semantic retrieval, and integration capabilities required for Agentic AI and intelligent automation.
Collaborate with Data Engineers, Data Scientists, Platform Engineers, Architects, and business stakeholders to deliver scalable platform capabilities.
Contribute to platform reliability, performance optimization, operational excellence, incident management, and cloud cost optimization activities.
Primary Skills (Must Have)
Platform Engineering & Cloud
AWS Cloud
Cloud-Native Architectures
Platform Engineering
Cloud Security & Networking
Scalability & Reliability Engineering
Distributed Systems Architecture
Software Engineering
Full Stack Development
React
TypeScript / JavaScript
Node.js
API Development
Microservices Architecture
Event-Driven Architecture
Testing Frameworks
DevOps & Automation
CI/CD Pipelines
Git
Infrastructure as Code (IaC)
Containerization
Automation Frameworks
Monitoring & Observability
Logging & Operational Excellence
Data & AI Platforms
Snowflake
Kafka
Data Warehousing
Data Governance
Data Quality Management
Semantic Layer Architecture
Large Language Models (LLMs)
Agentic AI
Model Context Protocol (MCP)
Retrieval-Augmented Generation (RAG)
Secondary Skills (Nice to Have)
Advanced Platform & Data Engineering
Internal Developer Platforms
Streaming Platforms
Data Cataloging
Data Lineage
Multi-Cloud Environments
AI & Data Engineering
MLOps
AI Platform Engineering
Model Serving
Apache Airflow
Astronomer
Apache Spark
Databricks
Domain Experience
Retail
eCommerce
Supply Chain
Customer-Facing Digital Platforms
Key Competencies
Strong focus on building scalable, reusable, enterprise-grade Data & AI platform capabilities.
Strong understanding of data products, semantic layers, data governance, AI governance, and enterprise data enablement.
Passion for delivering secure, trusted, and high-quality data products supporting analytics and AI.
Strong engineering mindset focused on automation, standardization, and platform innovation.
Adaptability to evolving Data, AI, Agentic AI, and cloud technologies.
Excellent stakeholder management and collaboration skills with the ability to translate business requirements into technology solutions.
Strong analytical and problem-solving capabilities.
Experience & Qualifications
Education
Required
Bachelor's Degree in Engineering or related discipline.
Preferred
Master's Degree in Computer Science, Information Technology, or related field.
Experience
Proven experience building and operating production-grade enterprise platforms and business-critical applications.
Strong expertise in designing and implementing cloud-native architectures, APIs, microservices, and distributed systems.
Hands-on experience developing scalable applications using React, TypeScript/JavaScript, and Node.js.
Experience building and operating solutions on AWS.
Experience supporting Data & AI Platform modernization initiatives.
Experience building reusable platform services, APIs, automation capabilities, and developer tools.
Strong understanding of platform security, identity management, governance, privacy, and compliance.
Experience implementing Git, CI/CD, Infrastructure as Code, automated testing, Agile delivery, and DevOps practices.
Experience supporting observability, reliability, performance optimization, and cloud cost management.
Retail, eCommerce, or large-scale customer-facing digital platform experience preferred.
Ways of Working
Hands-on contributor responsible for designing, building, and operating platform capabilities.
Collaborates closely with Data Engineers, Data Scientists, Architects, Platform Engineers, and business stakeholders.
Focused on delivering reusable, scalable, secure, and governed platform services across the enterprise Data & AI ecosystem.
Supports self-service analytics, AI enablement, intelligent data products, and enterprise platform modernization initiatives

Work arrangement
No