SHARATH REDDY

Hi 👋,
I'am SHARATH REDDY
Data Engineer & Mlops

Resume

About

I'am SHARATH REDDY

I’m a Data Engineer with a strong background in building and optimizing data pipelines, cloud architectures, and machine learning solutions. With expertise in technologies like GCP, Databricks, and Big Data frameworks, I specialize in turning complex datasets into actionable insights that drive business outcomes. Passionate about innovation and continuous learning, I aim to solve challenging problems through data-driven approaches.

Drop a mail sharathkumarreddy52@gmail.com

Education

B.Tech(Mechanical)

NBKR Institute of Science and Technology , Vidyanagar

Diplamo (Mechanical)

Government Polytechnic College, Anantapur

SSC

L.R.GHigh School,Anantapur

Experience

Data Engineer - Random Trees

July 2024 - Present

Designed and implemented data pipelines using BigQuery, SnapLogic, and ELT processes, improving data processing efficiency by 20%.
Managed and optimized workflows with Cloud Composer (Airflow), reducing task execution times by 30%.
Developed and maintained data models and schemas for scalable solutions, leveraging AWS S3 and Redshift for storage and querying.
Collaborated with cross-functional teams to integrate and optimize data sources, resulting in a 40% improvement in data availability.
Monitored and debugged data workflows, reducing system downtime by 15%, ensuring efficient and reliable data pipelines.

Data Engineer AI/ML - TrideMobility

Nov 2023 - Present

Built and optimized Databricks data pipelines for real-time processing and analysis of EV telemetry data, improving vehicle performance insights.
Implemented MLflow to manage the end-to-end machine learning lifecycle, including tracking experiments and deploying predictive maintenance models.
Designed efficient data ingestion workflows using Kafka and GCP Pub/Sub, enabling seamless handling of high-volume data streams from electric vehicles.
Leveraged Google Cloud Platform (GCP) tools such as BigQuery for storing and querying large datasets, enhancing data accessibility and analysis.
Developed and deployed anomaly detection algorithms for EV components and charging infrastructure, driving proactive maintenance strategies.

Data Science Intern - Code Clause

July 2023 - Aug 2023

Enhanced credit card fraud detection accuracy by 20% through proficient utilization of REST APIs, resulting in annual savings.
Implemented advanced movie recommendation algorithms, increasing user engagement and improving customer retention.
Achieved high accuracy in breast cancer classification by designing and fine-tuning machine learning models.
Collaborated with cross-functional teams, reducing project delivery time by 20% and boosting team productivity.
Conducted thorough data analysis, reducing false positives in the fraud detection system and optimizing detection efficiency.
Worked on the "Smart India Hackathon" project to develop a solution for COVID-19 detection using X-ray images.

Skills

Python

AWS

Machine Learning

Numpy

Pandas

BigData

Azure Databricks

MY SQL

Deep Learning

PySpark

MongoDB

Data Pipelines

Git

GCP

Projects

DESIGN AND ANALYSIS OF HELICAL GEAR UNDER DIFFERENT CONDITIONS

To estimate & identify the various stresses and other important parameters of helical gears, a three- dimensional solid model of set of gears is generated using the Computer Aided Design Software Pro/Engineer, which is currently being known as a strong tool for solid modeling. The analytical investigations are carried out by varying the load on gear.

A Mini project, (Team Lead) .

catiaV5| ANSYS |

online mental stress detection by using Machine Learning

stress detection by using by question format

Machine Learning | Python | Numpy | Pandas / HTML / CSS /DJANGO

• Engineered a machine learning-based system for online stress detection, attracting visitors within the first month to the stress prevention website. • Integrated ML models into web development, resulting in a stress detection tool with a user satisfaction rate and validating reduction in user-reported stress levels through surveys. • Achieved a 15% increase in user engagement by delivering interactive stress-relief content. • Enhanced the website's engagement metrics with an increase in user interaction and a 90% user satisfaction rate through the integration of ML models into web development. • Successfully reduced user-reported stress levels by 20%, validated through surveys.

completed