Data/QA Engineer _ AWS Cloud

1 day ago

Cochin, Kerala, India Inforizon Full time ₹ 1,04,000 - ₹ 1,30,878 per year

Job Description: QA Engineer (Agentic AI AWS)

Key Responsibilities

  • Develop and execute test plans, test cases, and automation scripts for AI-powered applications on AWS.
  • Test data pipelines (ETL/ELT, streaming, batch) for correctness, completeness, and performance.
  • Validate AI/ML workflows (model training, inference, fine-tuning, and retraining pipelines).
  • Perform functional, regression, integration, performance, and security testing of cloud-based applications.
  • Create automated test frameworks for APIs, microservices, and AWS Lambda functions.
  • Implement continuous testing practices within CI/CD pipelines.
  • Collaborate with data engineers, ML engineers, and solution architects to ensure end-to-end system quality.
  • Monitor production systems and validate results against expected business/AI outcomes.
  • Document test results, defects, and provide feedback for process improvements.

Required Skills & Qualifications

  • 3-7 years of QA/testing experience (depending on seniority level).
  • Strong knowledge of manual and automated testing practices.
  • Strong skills in Python/Java/JavaScript for automation.
  • Experience testing cloud-native applications on AWS (S3, Lambda, Step Functions, API Gateway, ECS, DynamoDB, etc.).
  • Familiarity with CI/CD pipelines (Jenkins, GitHub Actions, GitLab CI/CD, or CodePipeline).
  • Understanding of data validation and data quality frameworks (Great Expectations, Deequ, etc.).

Job Description: Data Engineer (Agentic AI AWS)

Key Responsibilities

  • Design, build, and maintain ETL/ELT pipelines for structured and unstructured data in AWS.
  • Work with AWS services (e.g., S3, Glue, EMR, Lambda, Kinesis, Step Functions, Redshift, Athena, DynamoDB, RDS) to manage data ingestion, transformation, and storage.
  • Ensure data quality, governance, and security across the full lifecycle.
  • Enable real-time and batch data processing to support AI-driven workflows.
  • Collaborate with AI/ML teams to prepare datasets for model training, inference, and fine-tuning.
  • Optimize data infrastructure for scalability, cost-efficiency, and performance.
  • Implement CI/CD pipelines and best practices for data engineering in a cloud-native environment.
  • Monitor and troubleshoot data pipelines to ensure high availability and reliability.

Required Skills & Qualifications

  • 37 years of experience in data engineering (adjust based on seniority).
  • Strong programming skills in Python, SQL, and/or Scala/Java.
  • Expertise in AWS cloud ecosystem (S3, Glue, EMR, Redshift, Athena, Kinesis, Lambda, etc.).
  • Experience with data pipeline orchestration tools (Airflow, Step Functions, Dagster, or similar).
  • Proficiency with big data frameworks (Spark, Hadoop, or Flink).
  • Familiarity with data modeling, warehousing, and schema design.
  • Solid understanding of data governance, lineage, and security (IAM, Lake Formation, encryption).
  • Experience with real-time streaming data (Kafka, Kinesis, or equivalent).
  • Knowledge of DevOps practices (Terraform/CloudFormation, CI/CD, Git, Docker)

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