Role Overview

We are seeking a highly skilled Cloud Data/AI Engineer to design, build, and optimize data pipelines, AI-driven solutions, and cloud-based architectures. This role is ideal for individuals with strong logical and analytical thinking skills and hands-on experience with ETL processes, dashboards, and data engineering using PySpark. Whether you’re a fresh graduate eager to launch your career or an experienced professional (senior roles available for candidates with 4+ years of experience), this is an exciting opportunity to work on Generative AI innovations and AWS native technologies in a hybrid work environment.

Key Responsibilities

• Data Pipeline and ETL Development:
• Build, optimize, and manage ETL pipelines using PySpark and AWS Glue for large-scale data processing.
• Design robust data workflows for processing structured and unstructured data.
• Ensure data integrity and security in all stages of processing.
• Dashboard and Data Visualization for Data engineer:
• Design and develop dashboards using tools like AWS QuickSight, Tableau, or Power BI.
• Collaborate with stakeholders to create insightful visualizations for data-driven decision-making.
• AI/ML Model Development and Deployment for AI engineer:
• Develop, deploy, and maintain AI/ML models using frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Implement models on cloud platforms using AWS SageMaker and automate model training and deployment pipelines.
• Cloud Infrastructure and Data Management:
• Architect and deploy scalable data solutions using AWS services like Redshift, EMR, and S3.
• Use Infrastructure as Code tools (e.g., Terraform, AWS CDK, or CloudFormation) to automate deployments.
• Performance Optimization:
• Optimize ETL pipelines, AI models, and data queries for performance, cost-efficiency, and scalability.
• Monitor data workflows and resolve bottlenecks proactively.
• Explore AWS and Generative AI Innovations:
• Gain hands-on experience with Generative AI tools and frameworks to create innovative data and AI solutions.
• Experiment with the latest AWS native technologies to enhance data pipelines and AI projects.
  

Requirements

• Education: Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field. Equivalent practical experience will also be considered.
• Hands-on experience with ETL pipelines using PySpark and data transformation tools like AWS Glue.
• Proficiency in building interactive dashboards with tools like Tableau, AWS QuickSight, or Power BI.
• Strong programming skills in Python (preferred) or other languages for data processing and AI/ML development.
• Familiarity with cloud platforms (AWS preferred) and services like S3, Redshift, and SageMaker.
• Strong logical and analytical thinking skills for solving complex data problems.
• Knowledge of SQL and database management systems.
• Fresh graduates with a passion for cloud data engineering and AI are encouraged to apply.
  
  

Preferred Skills

• Relevant AWS Certifications (e.g., AWS Certified Data Analytics, AWS Certified Machine Learning) are a strong plus.
• Senior candidates (4+ years) should demonstrate expertise in PySpark, dashboard development, large-scale data processing, and AI/ML model deployment.
• Familiarity with monitoring tools for data pipelines and AI workflows.
• Strong communication skills for collaborating across teams and presenting data insights.
  
  

Benefits

• Competitive salaries.
• Career growth opportunities.
• Flexible working hour.
• Attractive benefits.