Open to opportunities

Ayush Lokhande

@ayushlokhande

Message

I build scalable cloud ETL/ELT and LLM-powered analytics automation pipelines.

India

Message

What I'm looking for

I’m looking for a role where I can own cloud data pipelines end-to-end—ETL/ELT, data quality, and monitoring—while expanding LLM-driven automation for analytics. I want a team that values reliable data, fast iteration, and measurable impact.

I’m a results-driven Data Engineer focused on building scalable ETL/ELT pipelines that move businesses from raw data to trusted insights. I specialize in Azure and AWS cloud data platforms, with hands-on experience processing 10K+ daily transactions and optimizing SQL for measurable performance gains.

In my current role, I architected and deployed 8+ production ETL/ELT pipelines across Azure (Data Factory, Databricks) and AWS. I improved data latency from 6 hours to 45 minutes, built automated data validation frameworks with 98.5% accuracy, and reduced data incidents by 85% through monitoring dashboards and proactive issue resolution.

I also bring an AI-first mindset to data engineering—integrating LLM workflows and automating analytics tasks. Using LangChain and GPT-4, I’ve automated SQL query generation from natural language, reducing analyst query time by 65% and boosting productivity through validated, intelligent automation.

Beyond production work, I’ve built portfolio projects like an AI Data Analyst Agent (Python, LangChain, GPT-4, RAG) and a serverless EEG data processing pipeline on AWS Lambda and S3. I’m energized by teams that value data quality, fast feedback, and practical automation that turns complex systems into reliable outcomes.

Experience

Work history, roles, and key accomplishments

Current

Junior Data Engineer

Current

Intelebee LLC

Apr 2024 - Present (2 years 2 months)

Architected and deployed 8+ production ETL/ELT pipelines across Azure and AWS, cutting data latency from 6 hours to 45 minutes (87.5% improvement) across 5 enterprise systems. Built automated validation and quality monitoring, improving data accuracy to 98.5% and reducing incidents by 85% with resolution within a 15-minute SLA.

Azure Data Factory Azure SQL AWS S3 Databricks Pyspark ETL Data Validation SQL Query Optimization Data Systems & Monitoring

Data Scientist Intern

E-Zest Solutions

Jul 2024 - Sep 2024 (2 months)

Developed ML forecasting models (Random Forest, XGBoost) achieving 85% prediction accuracy on 100K+ transactions, supporting $200K projected revenue impact. Integrated LangChain + GPT-4 workflows to generate SQL from natural language, reducing analyst query time by 65% and improving reporting throughput.

Python Random Forest XGBoost Power BI SQL Generation Lang Chain GPT 4

Education

Degrees, certifications, and relevant coursework

Sage University

Bachelor of Technology (B.Tech), Artificial Intelligence & Machine Learning

2021 - 2025

Grade: CGPA: 7.59/10.0

Activities and societies: AI/ML Club member; participated in 2 college-level hackathons (Smart City Solutions, Healthcare AI).

Pursued a B.Tech in Artificial Intelligence & Machine Learning with coursework covering machine learning, deep learning, computer vision, databases, and cloud computing. Completed with CGPA 7.59/10.0 and participated in AI/ML club activities and college hackathons.