# Chiranjeevi Karthikeya

> Actively Seeking Opportunities | AI/ML Engineer at Cisco System | Ex-Hexaware Technologies | Expert in MLOps & DevOps, Machine Learning, NLP & GenAI, Cloud Platforms, APIs & Model Deployment, Computer Vision

Location: United States, United States
Profile: https://flows.cv/chiranjeevi

AI/ML Engineer with 4+ years of experience building scalable machine learning, deep learning, and AI solutions across networking, cybersecurity, banking, and insurance domains. Strong expertise in Python, TensorFlow, PyTorch, Spark, and cloud platforms (AWS, Azure, GCP) for building production-ready ML systems. Experienced in ML model development, feature engineering, data pipelines, and end-to-end MLOps deployment using Docker, Kubernetes, and CI/CD pipelines. Proven ability to develop predictive models, anomaly detection systems, NLP solutions, and computer vision pipelines that improve operational efficiency, reduce risk, and enhance decision-making. Skilled at collaborating with cross-functional teams to deliver scalable AI-driven solutions in enterprise environments.

## Work Experience
### AI/ML Engineer @ Cisco System
Jan 2024 – Present | United States
•	Developed and deployed machine learning models using Python, TensorFlow, and Scikit-learn to detect anomalies and security threats in enterprise network traffic, improving threat detection accuracy by 28% and reducing false positive alerts by 22%.
•	Designed scalable data pipelines using PySpark, SQL, and AWS services to process terabytes of real-time network telemetry data, reducing data processing latency by 35% and enabling faster model inference for production systems.
•	Built deep learning architectures including CNN and LSTM models for traffic classification and predictive network failure detection, reducing unexpected network downtime by 19% across monitored infrastructure environments.
•	Collaborated with network engineering, cybersecurity, and product teams to translate business requirements into AI solutions for automated incident detection, intelligent routing insights, and network capacity optimization.
•	Implemented MLOps pipelines using Docker, Kubernetes, and CI/CD workflows, enabling automated model retraining, version control, and reliable deployment with 99.8% production uptime.
•	Applied advanced feature engineering, dimensionality reduction, and hyperparameter tuning techniques to improve model precision by 31% while reducing inference latency by 25%.
•	Developed Explainable AI (XAI) frameworks using SHAP and LIME to interpret model predictions and improve trust in AI-driven security insights across engineering and operations teams.
•	Implemented model monitoring and drift detection pipelines using ML observability tools, enabling automated alerts and retraining strategies that reduced model degradation by 40%.
•	Produced detailed model documentation, architecture diagrams, and performance reports, while mentoring junior engineers on ML development standards, code optimization, and deployment best practices.

### Machine Learning Engineer @ Hexaware Technologies
Jan 2019 – Jan 2022 | India
•	Built machine learning solutions using Python, Scikit-learn, and XGBoost for banking and insurance automation, improving document classification accuracy by 35% and reducing manual review effort by 50%.
•	Designed predictive models for customer churn forecasting and insurance claim fraud detection, achieving AUC > 0.92 and generating approximately $1.2M annual operational savings for client organizations.
•	Developed computer vision systems using OpenCV and YOLO for automated document verification and OCR processing, achieving 95% accuracy in identity validation and KYC workflows.
•	Built scalable ML pipelines using Python, SQL, and Apache Spark to process structured and unstructured datasets from APIs and enterprise systems, enabling real-time analytics capabilities.
•	Implemented MLOps workflows using Airflow, MLflow, and Docker, enabling automated model tracking, versioning, and retraining pipelines deployed to Azure Machine Learning environments.
•	Collaborated with data engineering teams to design Snowflake feature stores, improving feature retrieval performance and accelerating model experimentation cycles by 30%.
•	Applied hyperparameter tuning techniques including GridSearchCV and Optuna, along with ensemble modeling strategies to improve model accuracy across classification and regression tasks.


## Education
### Master's degree in Information Technology
Southern New Hampshire University

### Bachelor's degree in Computer Science
Geethanjali College of Engineering and Technology


## Contact & Social
- LinkedIn: https://linkedin.com/in/c-karthikeya

---
Source: https://flows.cv/chiranjeevi
JSON Resume: https://flows.cv/chiranjeevi/resume.json
Last updated: 2026-04-16