ML Engineer - Time-Series & Anomaly Detection Expert

Ushmitha Annapaneni

ML Engineer

PROFESSIONAL SUMMARY:

Machine Learning Engineer with over 2 years of hands-on experience building and deploying production-grade ML pipelines that deliver actionable insights to optimize real-time decision-making and operational efficiency. Specialized in time-series analysis, anomaly detection, and deep learning, with proven success in designing scalable solutions for large datasets. Adept at model training, deployment, and monitoring workflows, consistently driving improvements in system performance and data accuracy. Collaborative team player, skilled at turning complex data into impactful, business-driven solutions.

TECHNICAL SKILLS:

Category

Skills & Tools

Programming & Data

Python, SQL, pandas, NumPy

Machine Learning

Supervised & Unsupervised Learning, Feature Engineering, Model Evaluation

Deep Learning

PyTorch, CNN, LSTM, Transformer Models

Time-Series & Analytics

Time-Series Analysis, Anomaly Detection, Statistical Analysis

MLOps & Deployment

Docker, Model Retraining, Data Drift Detection, ML Pipelines

Visualization & Reporting

Power BI, Tableau, Excel, Matplotlib

Tools & Platforms

Git, Linux, Jupyter Notebook, Windows

PROFESSIONAL EXPERIENCE:

Machine Learning Engineer

Western Union LLC (via CrownIT) – USA (Remote)

Jul 2023 – Present

Project: Real-Time Anomaly Detection for Sensor-Based Systems

Designed and deployed end-to-end ML pipelines for real-time monitoring of multivariate sensor data, enabling operational teams to make data-driven decisions.

Processed, validated, and preprocessed time-series datasets using Python and SQL, optimizing data workflows for large-scale systems.

Trained and optimized CNN and LSTM-based anomaly detection models, improving detection accuracy by 20% and reducing false positives, leading to faster system monitoring and lower downtime.

Developed and deployed automated model retraining and data drift detection workflows, ensuring consistent model performance and reliable production systems.

Containerized ML workflows using Docker, enabling reproducibility and scalability in training and deployment phases.

Collaborated with cross-functional teams in an Agile environment, contributing to faster model iterations and project completion.

Tech: Python, SQL, PyTorch, Docker, Pandas, NumPy, Linux, Git

Data Science Engineer

JooRa Drones Pvt. Ltd. – India

Jan 2022 – Mar 2023

Project: Drone-Based Environmental Data Analytics Platform

Analyzed large-scale environmental and telemetry data, providing actionable insights for real-time decision-making and system monitoring.

Applied statistical analysis and anomaly detection techniques to identify abnormal sensor behavior, enhancing system reliability.

Conducted time-series analysis to uncover trends and seasonal behavior, improving forecasting accuracy.

Automated data preprocessing and validation pipelines, reducing manual work by 30% and improving process efficiency.

Created visualizations and reports that helped engineering and operations teams make data-driven decisions, optimizing product performance.

Tech: Python, SQL, pandas, NumPy, Matplotlib, Linux, Git

Education:

M.S. Electrical & Computer Engineering

University at Albany, SUNY – 2025

Relevant Coursework: Machine Learning, Signal Processing, Embedded & Real-Time Systems

B.Tech Electronics & Communication Engineering

JNTU Kakinada – 2023

Relevant Coursework: Signal Processing, Digital Systems, Microprocessors

Certifications:

AWS Cloud Practitioner Essentials (Expected completion: Feb 2026)

Data Science in AI/ML Specialization

Python & Data Science — NPTEL

Introduction to Python Programming — Microsoft Learn

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