Manisai Konjarla

Orchestrating the development of enterprise-grade AI systems, focusing on scalable training and real-time data processing for global financial operations.
Architected a robust distributed ML pipeline using Apache Spark, processing 10TB of daily data to facilitate scalable training of complex model architectures.
Optimized storage demand forecasting by implementing and cross-validating 5 distinct algorithms, ensuring peak accuracy and precision for resource allocation.
Executed rigorous feature engineering using Pandas and NumPy, boosting F1-score by 18% for anomaly detection systems.
Deployed low-latency (sub-100ms) RESTful APIs to serve model predictions, handle 1000+ RPS, and integrated DevOps practices for continuous deployment.
Spearheaded the transition to CI/CD pipelines via Jenkins, reducing production deployment cycles from days to hours within an Agile framework.
Managed large-scale data collection and storage strategies using AWS S3 and EMR, handling datasets exceeding 100TB to prevent infrastructure drift.
Developed and troubleshot custom anomaly detection models in PyTorch, achieving 24-hour lead time on failure predictions with high recall.
Implemented a comprehensive model monitoring and A/B testing framework, reducing false positives by 30% through data-driven refinement.

Architected a robust distributed ML pipeline using Apache Spark, processing 10TB of daily data to facilitate scalable training of complex Model Architectures., Optimized storage demand forecasting by implementing and cross-validating 5 distinct algorithms, ensuring peak accuracy and precision for resource allocation., Executed rigorous Feature Engineering using Pandas and NumPy, boosting F1-score by 18% for anomaly detection systems., Deployed low-latency (sub-100ms) RESTful APIs to serve model predictions, handle 1000+ RPS, and integrated DevOps practices for continuous deployment., Spearheaded the transition to CI/CD pipelines via Jenkins, reducing production deployment cycles from days to hours within an Agile framework., Managed large-scale Data Collection and storage strategies using AWS S3 and EMR, handling datasets exceeding 100TB to prevent Infrastructure Drift., Developed and troubleshot custom anomaly detection models in PyTorch, achieving 24-hour lead time on failure predictions with high recall., Implemented a comprehensive Model Monitoring and A/B testing framework, reducing false positives by 30% through data-driven refinement.

Led the end-to-end lifecycle of NLP-based AI solutions, from initial exploratory data analysis to production deployment on cloud platforms.
Cleaned and preprocessed 500,000+ unstructured records using NLTK and spaCy, establishing high-quality data pipelines for sentiment analysis.
Conducted deep exploratory data analysis (EDA) to identify business-critical trends, translating raw feedback into actionable insights for product teams.
Leveraged transfer learning (BERT) and hyperparameter tuning to drastically reduce training time while maintaining 90%+ accuracy.
Performed complex cross-validation and evaluation metrics (Accuracy, Precision, Recall) to mitigate overfitting in text classification models.
Streamlined production deployment utilizing Google Cloud AI Platform and Docker, ensuring high availability and seamless model updates.
Orchestrated real-time data processing via Kafka and Spark Streaming, reducing data latency and enabling instant customer sentiment tracking.
Collaborated with cross-functional Agile teams to define KPIs, ensuring AI models adhered to project requirements and business goals.
Visualized key performance indicators in Tableau, providing leadership with interactive dashboards to monitor data drift and regional trends.

Cleaned and preprocessed 500,000+ unstructured records using NLTK and spaCy, establishing high-quality Data Pipelines for sentiment analysis., Conducted deep Exploratory Data Analysis (EDA) to identify business-critical trends, translating raw feedback into actionable insights for product teams., Leveraged transfer learning (BERT) and Hyperparameter Tuning to drastically reduce training time while maintaining 90%+ Accuracy., Performed complex cross-validation and Evaluation Metrics (Accuracy, Precision, Recall) to mitigate overfitting in text classification models., Streamlined Production Deployment utilizing Google Cloud AI Platform and Docker, ensuring high availability and seamless model updates., Orchestrated Real-time Data Processing via Kafka and Spark Streaming, reducing data latency and enabling instant customer sentiment tracking., Collaborated with cross-functional Agile teams to define KPIs, ensuring AI models adhered to project requirements and business goals., Visualized key performance indicators in Tableau, providing leadership with interactive dashboards to monitor data drift and regional trends.