Machine Learning Data Scientist

Vishnu Tallapareddy

Data Scientist (AI/ML Engineer)

+1-380-***-**** ******.*@*****.***

SUMMARY

With 4 years of experience in Data Engineering and AI/ML engineering, I specialize in building scalable cloud-based solutions and intelligent systems. Leveraging AI/ML frameworks like TensorFlow, PyTorch, and scikit-learn, I have developed advanced systems for medical image analysis, recommendation engines, and predictive analytics. My expertise includes designing and optimizing data pipelines for efficient ETL workflows and real-time processing, as well as implementing robust MLOps practices to ensure seamless deployment, monitoring, and lifecycle management of machine learning models on platforms such as AWS and GCP. I have achieved significant reductions in model size and latency through techniques like quantization, pruning, and A/B testing. In healthcare IT, I have integrated systems adhering to HL7 standards, ensured HIPAA compliance, and developed secure APIs to manage sensitive medical data. My experience in Agile environments includes leading cross- functional teams, driving iterative development, and delivering scalable, high-quality solutions that align with business objectives.

SKILLS

●Programming/Libraries: Python (Pandas, NumPy, Scikit-learn, TensorFlow, Hugging face Jupyter), PySpark

●Databases: SQL, MongoDB, Oracle, MySql

●Machine Learning: Predictive Modeling, Supervised and Unsupervised Learning, Anomaly Detection, Feature Engineering, LLM’s

●Algorithms: KNN, Regression (Linear, Logistic, Multiple), Naive Bayes, Random Forest, SVM, NLP, K- Means

●Cloud Platforms: AWS (Glue, Redshift, SageMaker, Lambda, Athena, S3, EMR, Kinesis, Firehose, IAM)

●Big Data & Workflow Automation: Apache Airflow, Spark, Hadoop, Kafka, ETL/ELT Pipelines

●Data Engineering: Data Extraction, Data Validation, Dimensional Modeling, Data Warehousing

●Data Visualization: Tableau, Looker Studio, Power BI

●Project Management: Workflow orchestration, Agile, Software Development Life Cycle, Work Breakdown Structure, Slack, Jira

●Version Control Tools: Git, GitHub, GitLab, Bitbucket

CERTIFICATES:

●AWS Machine Learning Specialty (In progress)

●AWS Data Engineer Associate

●AWS Educate Introduction to Generative AI

●Celonis EMS Technical Expert

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WORK EXPERIENCE:

Data Scientist (AI/ML Engineer) Broadcom July 2024 – Present

●Conducted statistical analysis and developed predictive models, reducing feature engineering workflows’ runtime by 20% and improving machine learning model training efficiency.

●Extracted and analyzed large datasets using PySpark, SQL, and AWS tools, generating actionable insights that led to a 15% increase in product performance and revenue optimization.

●Designed and implemented GPT-like large language model prototypes, achieving 10% efficiency gains over baseline transformer models and enabling scalable deployment for real-world applications.

●Applied transfer learning and fine-tuning techniques to LLMs, improving performance for domain-specific tasks like conversational AI, sentiment analysis, and document summarization.

●Conducted prompt engineering experiments to optimize LLM outputs, saving 15% in content generation costs.

●Leveraged Kafka to process over 10,000 events/second, building scalable and fault-tolerant data pipelines that reduced data latency by 25% and supported real-time analytics.

●Implemented natural language processing (NLP) pipelines for text classification, sentiment analysis, and recommendation systems.

●Stayed up to date with the latest AI/ML trends and technologies, integrating emerging techniques like deep learning, reinforcement learning, and transfer learning into projects.

●Applied statistical and machine learning techniques like regression, clustering, classification, and deep learning to drive business outcomes.

●Developed a cloud-native MLOps platform on AWS for scalable AI/ML deployment and management using Python, TensorFlow ensuring 99.9% availability while handling petabytes of medical imaging data.

●Utilized TensorFlow and PyTorch for deep learning analysis of medical images, achieving 90% accuracy on 100,000 X-ray/MRI scans. Conducted A/B testing to compare model performance for data-driven decision-making.

●Employed advanced optimization techniques such as quantization and pruning (TensorFlow-Model-Optimization) to reduce ML model size by 60% without sacrificing accuracy.

●Established a scalable data ingestion pipeline on AWS (S3, Glue, Athena) for processing and storing terabytes of medical data from diverse sources, enduring reliability and scalability.

●Utilized pre-built machine learning algorithms (scikit-learn, XGBoost) for predictive analytics on medical data, resulting in an 18% improvement in diagnosis accuracy.

●Coordinated cross-functional teams to resolve critical system issues, achieving 99.9% uptime and reducing 50% incident resolution time.

Programmer Analyst Cognizant, India February 2022 – June 2023

●Developed an AI-driven recommendation engine for a leading e-commerce conglomerate with Cognizant, used microservices architecture with Java and Spring Boot.

●Implemented advanced machine learning algorithms to personalize product recommendations based on user behavior and preferences, leading to a 35% reduction in response times.

●Employed advanced AI techniques like NLP and collaborative filtering to enhance product recommendations, leading to a 20% increase in customer engagement and conversion rates.

●Preparation of Understanding documents, creating knowledge repository for the application process and end to end flow docs.

●Implemented reinforcement learning techniques to optimize product recommendations over time, allowing the system to adapt and improve based on user feedback and interactions.

●Integrated Elasticsearch and Apache Kafka for real-time data processing, enabling the recommendation engine to dynamically adapt to changing user interactions and market trends.

●Perform test estimation, test planning, requirement analysis, test design, test execution, defect management and test closure activities.

●Utilized unsupervised learning algorithms such as clustering and dimensionality reduction to segment customers based on browsing and purchasing behavior, enabling more targeted and personalized recommendations.

●Conduct daily review meetings for the entire team to track and streamline the workflow.

●Leveraged machine learning models for sentiment analysis and trend forecasting to provide actionable insights to the e- commerce client, enabling data-driven decision-making and strategic planning.

Celonis Data Engineer TCS India April 2020 – January 2022

●Utilized SQL to query and analyze large datasets, generating actionable insights that supported process optimization and strategic decision- making.

●Assisted in identifying operational inefficiencies and recommending process improvements to enhance performance.

●Assisted in developing resource allocation models by performing data analysis with SQL, contributing to a 15% improvement in project delivery timelines.

●Used tools like Excel, Python, R, and statistical software to perform quantitative analysis and modeling.

●Designed and maintained dashboards to monitor key performance indicators (KPIs) using SQL and data visualization tools, enabling real- time decision-making for operations teams.

●Operated financial market research, collected data and performed analysis by using statistical software

●Performed sensitivity analysis and risk assessment using SQL and statistical techniques to evaluate the impact of variable changes on business outcomes, enhancing decision-making accuracy under uncertainty.

EDUCATION

●Master’s in Data Science from Franklin University, Columbus, OH – USA.

●Bachelor’s in Computer Science and Engineering from Narsimha Reddy Engineering College - INDIA.

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