AWS Data Engineer

23 hours ago

Remote, India Shelorve Full time ₹ 12,00,000 - ₹ 36,00,000 per year

As a Senior AWS Data Engineer, you'll architect and operate our serverless data platform—Glue, Step Functions, Lambda, S3, DynamoDB, EC2—while partnering with our AI team to productionize RAG pipelines and LLM-powered services.Core Responsibilities (70%)

  • Serverless ETL & Orchestration
  • Build, optimize, and maintain AWS Glue jobs for batch ETL
  • Orchestrate workflows with Step Functions and Lambda functions
  • Data Storage & Access
  • Design and tune DynamoDB schemas and S3 storage layouts
  • Manage Redshift/Athena data warehouses and query performance
  • Compute Provisioning
  • Provision and maintain EC2 instances for specialized batch or analytic workloads
  • Automate instance lifecycle and security via IaC
  • Quality & Monitoring
  • Implement data-quality checks with Great Expectations (or similar)
  • Configure CloudWatch metrics, dashboards, and alerts for end-to-end visibility
  • Infrastructure as Code
  • Define and maintain Terraform or CloudFormation modules for all data services

AI Enablement & Deployment (30%)

  • RAG Pipeline Integration
  • Ingest, embed, index, and retrieve knowledge using Pinecone, Weaviate, or equivalent
  • LLM Fine-Tuning & Serving
  • Fine-tune models (OpenAI GPT, Anthropic, LLaMA) and deploy endpoints via API Gateway/Lambda or containers
  • Develop prompt-engineering workflows and automated evaluation loops
  • MLOps Practices
  • Build CI/CD for model training and release (AWS CodePipeline, Jenkins, GitLab CI)
  • Ensure secure, scalable serving with Docker/ECS or EKS

Must-Have Qualifications

  • 4+ years of AWS data engineering: Glue, Step Functions, Lambda, S3, DynamoDB, EC2
  • Strong Python (boto3) scripting for automation
  • Terraform or CloudFormation expertise
  • Hands-on experience integrating RAG workflows or deploying LLM applications
  • Solid SQL and NoSQL data-modeling skills

Job Type: Part-time

Pay: ₹100, ₹150,000.00 per month

Expected hours: 25 per week

Experience:

  • AWS Glue: 1 year (Preferred)
  • AWS Lamda: 1 year (Preferred)
  • RAG Pipeline: 1 year (Preferred)

Work Location: Remote

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