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Revanth Kumar Gonuguntla

Data Scientist - AI/ML • g*********************@gmail.com • +15******240 • drivetube.ai/•••••

Professional Summary

Data Scientist - AI/ML with 6+ years of experience managing end-to-end ML lifecycles, building MLOps pipelines, and deploying GenAI/LLM solutions. Experienced in model development, production deployment, monitoring, and cross-functional collaboration across telecom and financial domains.

Technical Skills

Programming Languages: Python,R
Frameworks and Libraries: TensorFlow,PyTorch,scikit-learn,XGBoost,LangChain,Matplotlib,Seaborn
Databases: SQL,Snowflake,BigQuery,AWS Redshift,MySQL,MongoDB
Cloud and DevOps: AWS,Azure,GCP,Docker,Kubernetes,Azure Kubernetes Service,Google Kubernetes Engine,Jenkins,Azure DevOps,AWS CodePipeline,Terraform
Data and Analytics: Apache Spark,PySpark,Apache Airflow,Databricks,AWS Glue,Azure Data Factory,DBT,Databricks Feature Store,Microsoft Fabric,Palantir Foundry,Power BI,Tableau
Tools and Methodologies: Hugging Face Transformers,OpenAI,LlamaIndex
Retrieval & Vector Stores: FAISS,Pinecone
MLOps & Model Management: MLflow,DVC,Azure ML,AWS SageMaker
Streaming & Messaging: Apache Kafka,Azure Event Hubs,Pub,Sub
Monitoring & Observability: Grafana,Splunk,ELK Stack,AWS CloudWatch,Azure Monitor

Work Experience

AT&T
Dallas, TX
Data Scientist - AI/ML
JAN 2025 – Present
Worked on AI/ML for telecommunications: network telemetry, logs, and device metrics to enable anomaly detection, threat intelligence, and predictive maintenance.
Tech Stack: TensorFlow, PyTorch, scikit-learn, Apache Airflow, Databricks, Azure Data Factory, Apache Kafka, Spark, Azure ML, AWS SageMaker, Databricks Feature Store, Snowflake, Hugging Face, LangChain, OpenAI, Microsoft Fabric, Palantir Foundry, Kubernetes, Docker, MLflow
  • Designed and deployed ML models for network anomaly detection and predictive maintenance using TensorFlow and PyTorch, reducing unplanned downtime and improving fault detection coverage.
  • Built automated ETL pipelines with Apache Airflow, Databricks, and Azure Data Factory to process large telemetry and log datasets, cutting manual data processing time by 40%.
  • Implemented real-time streaming analytics with Apache Kafka, Spark Streaming, and Azure Event Hubs to enable low-latency threat detection and operational alerts.
  • Integrated LLMs and GenAI workflows (Hugging Face, LangChain, OpenAI) to automate network documentation and intelligent alert summarization, lowering support team workload by ~30%.
  • Managed feature engineering and storage using Databricks Feature Store and Snowflake; applied time-series models (Prophet, ARIMA, LSTM) to forecast capacity and optimize maintenance schedules, reducing operational costs by 10%.
  • Enforced data governance and security for sensitive network datasets using Microsoft Fabric and Palantir Foundry; deployed models via Azure ML, SageMaker, and Kubernetes with CI/CD for reproducible production rollouts.
Global Atlantic Financial Group
Buffalo, NY
Data Scientist - AI/ML
FEB 2024 – DEC 2024
Delivered ML and GenAI solutions for financial services: predictive maintenance for machinery data, customer churn, fraud and document analysis for investment and compliance workflows.
Tech Stack: scikit-learn, TensorFlow, LangChain, Hugging Face, LlamaIndex, AWS Glue, Databricks, Snowflake, AWS SageMaker, Kubernetes, AWS Redshift, Athena, Apache Spark, Kafka, DBT
  • Developed churn prediction and credit-risk models using scikit-learn and TensorFlow; improved model accuracy by 25% through feature engineering and hyperparameter tuning.
  • Built RAG-based financial document analysis pipelines (LangChain, Hugging Face, LlamaIndex) integrating market data to enable context-aware risk assessment and regulatory summarization.
  • Designed and deployed Generative AI solutions to produce financial summaries and automated market reports, supporting portfolio teams and reducing manual reporting effort.
  • Implemented ETL and data governance workflows with AWS Glue, Databricks, and Snowflake to standardize financial data and improve downstream model reliability.
  • Engineered real-time ML inference pipelines on AWS SageMaker and Kubernetes to deliver low-latency financial predictions for fraud detection and trading signals.
  • Developed recommendation systems using collaborative filtering and matrix factorization to increase customer engagement by ~30% and supported A/B testing for feature rollouts and model validation.
Tata Consultancy Services
Hyderabad, Telangana
Data Scientist - MLOps
March 2020 – AUG 2023
Delivered MLOps and production ML capabilities for enterprise clients; focused on model lifecycle automation, monitoring, IaC and containerized deployments across AWS/Azure environments.
Tech Stack: AWS SageMaker, AWS EC2, AWS S3, CloudFormation, Terraform, Docker, Kubernetes, Jenkins, Azure DevOps, MLflow, DVC, PySpark, Databricks, XGBoost
  • Orchestrated end-to-end MLOps pipelines integrating training, versioning, and deployment for multiple ML models using AWS SageMaker, Docker, and Kubernetes.
  • Implemented CI/CD for ML workflows with Jenkins, CodePipeline, and Azure DevOps to automate testing, packaging, and production rollouts, improving deployment consistency.
  • Built monitoring and model-health solutions using MLflow, logging, and alerting frameworks to detect drift and performance regressions, enabling proactive retraining.
  • Applied Infrastructure as Code (CloudFormation, Terraform) to provision repeatable cloud environments and reduce environment setup time for data science teams.
  • Developed and deployed supervised models including XGBoost and neural networks; executed feature engineering and EDA at scale using PySpark and Databricks.
  • Established model versioning, reproducibility, and rollback processes using Git, DVC, and MLflow; collaborated with cross-functional teams to align MLOps practices with business SLAs.

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