Bus Proc And Strat Senior Analyst

Job Summary

We are looking for an Associate Data & AI Engineer (G9) with a solid foundation in data engineering on Azure and growing experience in Generative AI technologies such as RAG and LLM-based solutions.

This is an individual contributor role suited for a candidate who is eager to expand their expertise across data engineering and AI, working on modern data platforms and emerging AI use cases.

The ideal candidate is hands-on, curious, and adaptable, with the ability to contribute to both data pipeline development and AI-driven solutions.

Key Responsibilities

Data Engineering

• Support the design and development of data pipelines using Azure Data Factory, Databricks, and related services.
• Develop and maintain data transformations and workflows using SQL and PySpark.
• Work with structured and unstructured data to support analytics and AI use cases.
• Assist in implementing data models and optimizing data processing performance.
  
  

• Contribute to building Generative AI solutions using LLMs (e.g., Azure OpenAI, OpenAI).
• Assist in developing RAG pipelines (data ingestion, embeddings, retrieval).
• Support implementation of basic Agentic AI workflows using frameworks like LangChain or similar.
• Participate in prompt engineering and testing of AI applications.
  
  

• Use Azure Databricks for data processing and transformation tasks.
• Develop and maintain PySpark-based pipelines under guidance from senior team members.
  
  

• Write Python code for data processing, automation, and basic ML workflows.
• Assist in data preparation, feature engineering, and model evaluation.
  
  

• Work closely with senior engineers, architects, and stakeholders on project delivery.
• Actively learn and adopt new technologies in Azure and Generative AI.
• Contribute to documentation and knowledge sharing within the team.
  
  

• 3–5 years of experience in data engineering, software engineering, or AI/ML roles.
• Hands-on experience with Python.
• Working knowledge of SQL and data handling.
• Exposure to Microsoft Azure (Data Factory, storage, or compute services).
• Basic experience with Azure Databricks or PySpark.
• Exposure to Generative AI concepts, such as:
• LLMs and prompt engineering
• Basic understanding of RAG workflows
• Experience with data pipelines (ETL/ELT) concepts.
  
  

• Hands-on experience with Azure OpenAI or similar LLM APIs.
• Basic understanding of machine learning concepts and libraries.
• Experience with Power BI or data visualization tools.