Data Engineer (Python, Data Systems & AI Enablement) - Part-Time

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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