Machine Learning Engineer

Govardhan Sai Beeraka

Newark, New Jersey, ***** +1-908-***-**** *******************@*****.*** LinkedIn EXPERIENCE

UpGrad INSOFE Hyderabad, IN

Machine Learning Engineer Oct, 2022 – Apr,2023

Text Summarization:

• Utilized web scraping methods to obtain and process internet data, executing data preprocessing to build structured datasets for training purposes.

• Developed and applied text summarization models based on BERT, NLTK, T5 and Hugging Face that were subsequently exposed through APIs, ensuring effective integration. Speech-to-Text:

• Worked with multiple NLP models for speech recognition before selecting and fine-tuning Whisper-large

(Hugging Face) for accurate transcription.

• Optimized model inference and integrated the solution into production, ensuring scalability and real-time processing capabilities.

• Built and deployed Python REST APIs using Flask and FastAPI for seamless model integration. TuringMinds Hyderabad, IN

Machine Learning Engineer Trainee and Internship:Oct, 2021 – Sep, 2022

• Machine Learning & Predictive Modeling: Developed and deployed ML models using Decision Trees, Random Forest, and XGBoost for structured data modeling and predictive analytics. Built predictive models for fraud detection, customer attrition, and backorder fulfillment, leveraging ensemble methods. Applied Exploratory Data Analysis (EDA) and statistical techniques to extract insights and enhance model accuracy.

• Deep Learning & Computer Vision: Developed skin disease detection models using deep learning techniques, leveraging computer vision for medical image analysis.

• Big Data & Distributed Systems: Processed and optimized large-scale datasets using Apache Spark, Hadoop, and Docker, improving efficiency in machine learning pipelines.

• Data Visualization & Business Insights: Created automated dashboards in Tableau to track data patterns, providing insights for evidence-based decision-making across various business areas.

• MLOps & Cloud Deployment: Designed and deployed MLOps architectures, ensuring automation, monitoring, and scalability for machine learning pipelines. Gained hands-on experience in creating CI/CD pipelines with Jenkins, Docker, and Kubernetes. Deployed ML models to AWS and Azure, optimizing workloads to cloud environments. KEY SKILLS

• Machine Learning & Predictive Analytics: Decision Trees, Random Forest, XGBoost, Statistical Modeling, Time Series Analysis, Clustering, Ensemble Methods, OpenCV, Generative AI.

• Deep Learning & NLP: Transformer Models, OpenAI GPT, BERT, T5, Whisper, Retrieval- Augmented Generation (RAG), Few-Shot Prompting, Supervised Fine-Tuning (SFT).

• Data Engineering & Big Data: Apache Spark, Hadoop, Databricks, SQL, AWS/GCP Cloud.

• Software Development & APIs: Python, Flask, FastAPI, REST API development.

• MLOps & DevOps: CI/CD Pipelines, Git, Docker, Kubernetes, Jenkins, Cloud Deployments.

• Data Visualization & Analytics: Tableau, Matplotlib, Seaborn, Plotly. EDUCATION

NJIT - Ying Wu College of Computing Newark, NJ

Master of Science (MS) in Data Science Jan, 2024 – May 2025 UpGrad INSOFE, Case Western Reserve University (CWRU)Hyderabad, India Post Graduate Program in Computational Data Science(PGP - CDS)Oct, 2021 – Oct 2022 KKR & KSR Institute of Technology and Sciences Guntur, India Bachelor of Technology (BTech) in Computer Science Jul, 2017 - Oct, 2021 PROJECTS

Starcoder2 Instruction Pair Generation:

• I refined the Starcoder2 language model to automate the creation of instruction-response pairs, improving the efficiency of model training and adaptation for various tasks.

• Incorporated LangChain to simplify fine-tuning processes, aiding in the development of generative AI applications and optimizing large-scale language models. AWS Parallel Wine Quality Prediction Pipeline:

• Created and implemented a Spark-based predictive analytics pipeline on AWS, utilizing Docker for containerized execution, which allows for parallel processing of large datasets.

• Showcased proficiency in big data technologies (Spark/Hadoop) and MLOps best practices, ensuring effective, scalable, and reproducible machine learning workflows in cloud settings. CERTIFICATIONS

Post Graduate Certificate in Computational Data Science (INSOFE & Case Western Reserve University)

Machine Learning, Python, R Programming – LinkedIn AI for Everyone (Deeplearning.AI), Python for Everyone (University of Michigan)– Coursera

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