Data Engineer (AI Enablement)

Key Responsibilities

• Build and maintain scalable data pipelines using Python
• Write production-grade Python code specifically for data processing, transformation, and ETL workflows
• Perform data cleaning, preprocessing, and feature preparation for analytics and AI use cases
• Use data analysis and manipulation tools to handle large datasets efficiently
• Develop reusable Python modules for data ingestion and pipeline automation
• Perform exploratory data analysis (EDA) to understand data patterns and quality issues
• Optimize data workflows for performance, scalability, and reliability
• Support data requirements for AI/ML and Generative AI systems
• Build data services and APIs to support downstream AI applications
• Ensure data quality, consistency, and observability across pipelines

Must have skills

Python & Data Libraries (Hands-on Experience Mandatory)

Candidates must have solid practical experience with

• Pandas — data manipulation, transformation, and analysis
• NumPy — numerical operations and array-based processing
• Matplotlib — data visualization and reporting
• scikit-learn — basic ML workflows and model evaluation
• PyTorch — deep learning and AI model experimentation

AI / Generative AI Enablement

• Prepare and structure datasets for ML and LLM-based systems
• Support integration of AI models into data pipelines and applications
• Enable workflows for Generative AI use cases (RAG systems, agent workflows)
• Work with multiple AI model providers:
• OpenAI
• Anthropic
• LLaMA
• Mistral
• Exposure to AI orchestration frameworks such as LangChain, AutoGen, and CrewAI

Core Requirements

• Solid hands-on Python coding expertise focused on data systems (critical requirement)
• Ability to write clean, efficient, production-grade Python code
• Thorough understanding of data structures, ETL pipelines, and data workflows
• Experience working with large-scale structured and unstructured data
• Solid SQL skills for data extraction and manipulation
• Understanding of data modeling and analytics workflows
• Ability to support end-to-end data-to-AI pipelines

Good to Have

• Experience with big data or distributed processing systems
• Understanding of vector databases and embedding-based retrieval systems
• Experience building APIs or services for data/AI systems
• Familiarity with cloud platforms (AWS, Azure, GCP)
• Exposure to production monitoring and data observability tools