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Aspiring Data Scientist and AI/ML Engineer with hands-on experience building end-to-end ML pipelines, RAG-based LLM applications, and scalable data infrastructure. Skilled in Python, SQL, PySpark, LangChain, FastAPI, and Docker, with experience across GCP, Azure, and Snowflake. Currently pursuing a Master’s in Data Science and Innovation at UTS Sydney, with a strong track record of deploying production-ready ML systems (FastAPI/Streamlit), building layered ELT pipelines with Airflow/dbt, and delivering analytics-ready datasets and dashboards.…Aspiring Data Scientist and AI/ML Engineer with hands-on experience building end-to-end ML pipelines, RAG-based LLM applications, and scalable data infrastructure. Skilled in Python, SQL, PySpark, LangChain, FastAPI, and Docker, with experience across GCP, Azure, and Snowflake. Currently pursuing a Master’s in Data Science and Innovation at UTS Sydney, with a strong track record of deploying production-ready ML systems (FastAPI/Streamlit), building layered ELT pipelines with Airflow/dbt, and delivering analytics-ready datasets and dashboards.

PRATHAMESH NEMADE

Data Scientist, AI Engineer, Programmer, +2

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Aspiring Data Scientist and AI/ML Engineer with hands-on experience building end-to-end ML pipelines, RAG-based LLM applications, and scalable data infrastructure. Skilled in Python, SQL, PySpark, LangChain, FastAPI, and Docker, with experience across GCP, Azure, and Snowflake. Currently pursuing a Master’s in Data Science and Innovation at UTS Sydney, with a strong track record of deploying production-ready ML systems (FastAPI/Streamlit), building layered ELT pipelines with Airflow/dbt, and delivering analytics-ready datasets and dashboards.…Aspiring Data Scientist and AI/ML Engineer with hands-on experience building end-to-end ML pipelines, RAG-based LLM applications, and scalable data infrastructure. Skilled in Python, SQL, PySpark, LangChain, FastAPI, and Docker, with experience across GCP, Azure, and Snowflake. Currently pursuing a Master’s in Data Science and Innovation at UTS Sydney, with a strong track record of deploying production-ready ML systems (FastAPI/Streamlit), building layered ELT pipelines with Airflow/dbt, and delivering analytics-ready datasets and dashboards.

Available to hire

Aspiring Data Scientist and AI/ML Engineer with hands-on experience building end-to-end ML pipelines, RAG-based LLM applications, and scalable data infrastructure. Skilled in Python, SQL, PySpark, LangChain, FastAPI, and Docker, with experience across GCP, Azure, and Snowflake.

Currently pursuing a Master’s in Data Science and Innovation at UTS Sydney, with a strong track record of deploying production-ready ML systems (FastAPI/Streamlit), building layered ELT pipelines with Airflow/dbt, and delivering analytics-ready datasets and dashboards.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

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Expert

Intermediate

Intermediate

Intermediate

Beginner

Beginner

Work Experience

Machine Learning Intern at Accordo Ai

October 1, 2024 - December 31, 2024

Integrated AI solutions into procurement workflows to improve process efficiency and support strategic decision-making. Analyzed procurement datasets using Python and SQL to surface trends and anomalies and refine sourcing strategies. Embedded ML models into client platforms and gathered requirements to deliver targeted AI solutions.

Data Analyst Trainee at Ruchi Energy Solution Pvt. Ltd.

January 1, 2024 - October 31, 2024

Analyzed power quality data from Unipower AB meters, identifying anomalies in harmonics, voltage fluctuations, and power losses. Built real-time Power BI dashboards for energy consumption monitoring and applied statistical techniques to reduce testing time by 10%. Conducted market research into power quality trends to support client advisory and technology insights.

Internship Trainee at Wartsila

September 1, 2023 - December 31, 2023

Developed a collaborative filtering model for trainee feedback analysis using machine learning. Established master data records using Informatica, AWS, and Azure and categorized feedback into structured feature values. Used internal and external data sources to create comprehensive master records for improved insights.