Junior Data Engineer with a strong foundation in Python, SQL, and AWS data services. The ideal candidate has exposure to Generative AI, LLMs, RAG, and automation workflows. This role blends data engineering with AI engineering to build scalable, secure, and intelligent data systems.

Roles and Responsibilities

1. Design and support batch and streaming data pipelines for scalable data processing.
2. Develop and maintain ETL/ELT workflows using Python and SQL for structured and unstructured data.
3. Work with AWS Data Services (e.g., S3, Redshift, Glue, Lambda) for cloud-native data solutions.
4. Integrate Generative AI capabilities such as LLMs and Retrieval-Augmented Generation (RAG) into data pipelines.
5. Support deployment and performance tuning of MCP (Model Compute Processing) servers.
6. Build automated workflows for data ingestion, transformation, and delivery.
7. Contribute to AI engineering initiatives, enabling intelligent data-driven automation.
8. Work with RESTful APIs to extract and integrate third-party data.
9. Implement secure coding standards and ensure resilience against OWASP vulnerabilities.
10. Collaborate with cross-functional teams in Agile environments using Scrum or Kanban.

Desired Candidate

1. 6-Months to 2 year hands-on experience in Python and SQL.
2. Basic understanding of Generative AI, LLMs, and RAG pipelines.
3. Knowledge of cloud infrastructure and experience with AWS Data Services.
4. Familiarity with ETL/ELT design, data modeling, and data quality assurance.
5. Experience working with MCP servers or deploying ML/AI workloads in production.
6. Exposure to AI engineering practices and automating data workflows.
7. Understanding of RDBMS, OLTP/OLAP systems, query optimization, and performance tuning.
8. Good grasp of RESTful APIs, secure development practices, and data privacy standards.
9. Excellent communication skills and ability to collaborate across technical teams.