AI Platform Data Engineer

Amazon

3 - 8 years

20 - 25 Lacs

bengaluru

Posted:2 days ago| Platform:

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Skills Required

data modeling coding infrastructure management workflow test cases business intelligence analytics monitoring sql python

Work Mode

Work from Office

Job Type

Full Time

Job Description

This role will be responsible for building and maintaining efficient, scalable, and privacy/security-compliant data pipelines, curated datasets for AI/ML consumption, and AI-native self-service data platforms using an AI-first development methodology
You will act as a trusted technical partner to business stakeholders and data science teams, deeply understanding their needs and delivering well-modeled, easily discoverable data optimized for their specific use cases
You are expected to default to AI-powered solutions, leverage agentic frameworks, and build systems that continuously learn and improve through AI accelerating development velocity, improving data quality, and enabling stakeholder independence through intelligent automation

Basic qualifications

3+ years of data engineering experience with demonstrated stakeholder management and communication skills
Experience with data modeling, warehousing and building ETL pipelines for both analytics and ML use cases
Experience with SQL and at least one programming language (Python, Java, Scala, or NodeJS)
Experience building datasets or features for machine learning models or self-service analytics
Extensive hands-on experience with Gen AI enhanced development pipelines, AI coding assistants (GitHub Copilot, Amazon Q, Cursor, etc.), and prompt engineering
Demonstrated track record of building AI agents, agentic workflows, or AI-powered automation tools
Demonstrated ability to build tools, frameworks, or platforms that enable others
Preferred qualifications
Experience with AWS technologies like Bedrock, SageMaker, Redshift, S3, AWS Glue, EMR, Athena, Kinesis, FireHose, Lambda, Step Functions, SageMaker Feature Store, and IAM roles and permissions
Experience building multi-agent systems, LangChain/LangGraph applications, or custom AI agent frameworks
Experience with prompt engineering, RAG (Retrieval Augmented Generation) systems, and LLM fine-tuning
Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS, with production-quality code standards
Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, vector databases, column-family databases)
Experience with BI tools (QuickSight, Tableau, Looker) and designing datasets for analytical consumption
Experience building or contributing to AI-native self-service data platforms, feature stores, or intelligent data cataloging systems
Experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn, LangChain) and feature engineering best practices
Experience with orchestration tools (Airflow, Step Functions, MWAA) and AI-powered workflow automation
Experience with infrastructure-as-code (CDK, Terraform, CloudFormation) and AI-assisted infrastructure management
Experience with AI-powered monitoring, observability, and anomaly detection platforms
Experience with API development, microservices architecture, and AI-enhanced API generation
Experience with semantic search, vector databases, and knowledge graph technologies
Experience facilitating technical workshops, training sessions, or serving in customer-facing technical roles
Knowledge of CI/CD practices for data pipelines, ML models, and AI agent deployment

A day in the life

Lead AI-assisted stakeholder engagement sessions using AI tools to synthesize requirements, generate technical specifications, and create stakeholder-ready documentation from Business Stakeholders, ML Scientists, and Data Scientists across verticals such as Subscriptions, Security & Compliance, Sales, Reverse Logistics, Fice, Product, Marketing, and Customer Support
Design and build curated datasets for analytics, feature stores, training datasets, and inference pipelines leveraging AI code generation, AI-powered data profiling, and automated feature engineering tools to accelerate delivery
Build new data ingestions and pipelines using AI-assisted development (Amazon Q, custom AI agents) with Native AWS services (Kendra, Glue, EMR, Step Functions, Lambda, SageMaker) and internal Amazon tools letting AI generate boilerplate code, optimize queries, and suggest architectural improvements
Maintain/improve existing data pipelines using AI-powered code analysis, automated refactoring tools, and intelligent monitoring systems that proactively identify optimization opportunities and data quality issues
Evaluate, pilot, and migrate to AI-native platforms and tools replacing traditional development workflows with agentic frameworks, AI-powered orchestration, and intelligent automation that continuously improves system performance
Build/improve/maintain AI-powered self-service platforms that use natural language interfaces, automated data discovery, intelligent query optimization, and conversational AI to enable stakeholders to independently access and consume data without technical expertise
Create intelligent goverce and observability systems that automatically classify data, detect PII, enforce access policies, track lineage, and provide AI-generated insights into system health
Develop APIs and automation frameworks that are themselves AI-enhanced using agents to generate API specifications, create test cases, optimize performance, and maintain documentatio

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