Full Stack AI and Data Engineer - AWS
New
J
JobgetherAI and data engineering
Fully remote position for candidates based in India.ContractMiddle
Salary not disclosed
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Job Details
- Experience
- 5+ years of overall software or data engineering experience
- Required Skills
- AWSPythonRESTful APIsGenerative AIPySpark
Requirements
- Have 5+ years of overall software or data engineering experience.
- Bring hands-on expertise across AWS, Python, modern data engineering, and AI or GenAI technologies.
- Have strong Python development skills and substantial AWS development experience.
- Have practical experience building data pipelines and ETL processes.
- Have experience with PySpark and strong knowledge of S3 and Athena or equivalent cloud data-lake technologies.
- Have experience with workflow orchestration such as AWS Step Functions and designing reliable, scalable data-processing architectures.
- Have proven experience developing REST APIs and backend services, including enterprise-system and third-party API integrations.
- Have hands-on experience developing LLM/GenAI applications or AI agents, including practical experience with Amazon Bedrock.
- Understand software engineering practices including Git, CI/CD, clean-code principles, testing, deployment, security, and production operations.
- Be able to work independently and collaborate across technical domains in a hands-on engineering environment.
Responsibilities
- Design and develop scalable data pipelines using Python, PySpark, AWS Glue, and AWS services.
- Build and orchestrate data workflows with AWS Step Functions and establish S3 data layers for raw, processed, and curated datasets.
- Maintain Athena and Glue Catalog layers for querying and downstream consumption.
- Develop Python backend services, REST APIs, and modular microservices for AI agents, front-end applications, and integrations.
- Implement backend authentication, input validation, error handling, logging, monitoring, and reliable deployment.
- Build AI agents using Amazon Bedrock, AgentCore, Strands, and related technologies.
- Implement agent tools, function calling, workflows, context management, and RAG where appropriate.
- Apply prompt management, AI evaluation, security, observability, and cost optimization practices; develop reusable agent components.
- Develop modular, reusable, testable code and contribute to CI/CD, security, access-control, and Infrastructure as Code practices.
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