Data Engineer (Python, Data Systems & AI Enablement)

Role Overview
Python-focused Data Engineer with strong hands-on coding skills in data-intensive systems. The role focuses on building scalable data pipelines,processing large datasets, and enabling AI/Generative AI applications through well-structured data infrastructure.
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, pre processing, 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

Required Python & Data Libraries (Hands-on Experience Mandatory)
Candidates must have strong 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

. Py Torch - deep learning and AI model experimentation

AI /Generative AI Enablement

. Prepare and structure datasets for M Land 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 frame works such as Lang Chain, AutoGen, and CrewAI

Core Requirements

. Strong hands-on Python coding expertise focused on data systems (critical requirement)

. Ability to write clean, efficient, production-grade Python code

. Strong understanding of data structures, ETL pipelines, and data workflows

. Experience working with large-scale structured and unstructured data

. Strong SQL skills for data extraction and manipulation

. Understanding of data modeling and analytics workflows

. Ability to support end-to-end data-to-AI pipelines

Preferred /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

What Success Looks Like

. High-quality Python code powering scalable data pipelines

. Reliable, clean, and well-structured datasets for AI systems

. Efficient ETL workflows with minimal manual intervention

. Seamless support for ML and GenAI applications in production

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