Software Engineer Machine Learning

Bhavana Sunkara

Software Engineer Machine Learning Engineer AI & Data Solutions

Charlotte, NC +1-940-***-**** ****************@*****.*** Bhavana Bhavana SUMMARY

Machine Learning Software Engineer with 3+ years of experience designing and deploying production-ready ML solutions across finance, healthcare, and enterprise domains. Skilled in NLP/LLMs (BERT, GPT, Hugging Face), computer vision, and predictive modeling using PyTorch and TensorFlow. Experienced in MLOps (Docker, Kubernetes, MLflow, Triton) and distributed data pipelines with Spark, Hadoop, and Kafka. Proficient in cloud platforms (AWS, Azure, GCP), with a proven track record of scaling models to millions of users, reducing inference latency, and ensuring compliance through monitoring, drift detection, and model governance. SKILLS

Programming Languages: Python, R, Java, C++, Scala, SQL, NoSQL, MATLAB, Bash/Shell NLP & LLMs: Natural Language Processing (NLP), Large Language Models (LLMs), Transformers (BERT, GPT), Hugging Face, SpaCy, Text Analytics, Sentiment Analysis, Topic Modeling

Machine Learning & AI: Supervised/Unsupervised Learning, Deep Learning, Generative AI, Predictive Modeling, Feature Engineering, Hyperparameter Tuning, Model Training/Validation, Regression & Classification, Clustering, Reinforcement Learning, Recommendation Systems

Computer Vision: OpenCV, TensorFlow, PyTorch, Convolutional Neural Networks (CNNs), Image Segmentation, Object Detection, Image Processing

Big Data & Databases: Apache Spark, Hadoop, ETL & Data Pipelines, Distributed File Systems, MongoDB, DynamoDB, Kafka Cloud Platforms: AWS (SageMaker, EC2, S3, Lambda), Google Cloud (GCP), Microsoft Azure MLOps & DevOps: Docker, Kubernetes, Jenkins, Terraform, Git/GitHub, MLflow, TensorBoard Deployment & Monitoring: Model Deployment (MLflow, Kubeflow, Triton, WindowsML), CI/CD Pipelines, Model Drift Detection, Performance Monitoring

PROFESSIONAL EXPERIENCE

JPMorgan Chase, USA Aug 2024 – Current

ML Software Engineer

• Deployed real-time fraud detection models (PyTorch, Scikit-learn) on AWS SageMaker, improving fraud detection accuracy by 23% and reducing false positives across 10M+ daily transactions.

• Designed end-to-end ML pipelines (Spark, Hadoop, Kafka) that processed terabytes of financial data, reducing data preparation time by 35%.

• Built and fine-tuned LLMs (BERT, GPT) for sentiment analysis and compliance document classification, achieving 92% accuracy and cutting manual review time by 40%.

• Optimized model inference latency by deploying ML models with NVIDIA Triton Inference Server and Kubernetes, reducing response time by 45% and ensuring scalable, low-latency model deployment for high-volume financial transactions.

• Implemented MLOps best practices (automated retraining, hyperparameter tuning, versioning) reducing deployment cycle time by 30% and ensuring regulatory compliance.

• Partnered with data engineers and analysts to convert raw financial data into actionable insights, accelerating risk assessments and forecasting.

• Implemented real-time model monitoring and anomaly detection pipelines using MLflow, Prometheus, and Python automation scripts, enabling proactive model drift detection and reducing financial risk exposure by 20% in production ML systems.

• Conducted model validation & compliance testing in line with JPMorgan’s governance policies to ensure fairness, interpretability, and adherence to regulatory standards. Accenture, India Jan 2021 – Jan 2023

Software Engineer (Machine Learning & Data Science Projects)

• Built Spark & Hadoop-based ETL pipelines to preprocess billions of records, improving ML model readiness and scalability by 40%.

• Developed and deployed predictive models (classification, regression, forecasting) using XGBoost & TensorFlow, improving client KPIs such as customer retention (+18%) and sales forecast accuracy (+22%).

• Automated NLP pipelines (sentiment analysis, topic modeling) with SpaCy & Hugging Face, reducing manual text review workload by 60%.

• Streamlined ML experimentation tracking with MLflow & TensorBoard, improving reproducibility and cutting model tuning time by 25%.

• Partnered with finance and retail clients to deploy AI-powered applications into production, ensuring compliance and performance at enterprise scale.

EDUCATION

Master of Science in Data Science 2023 - 2024

University Of North Texas, Texas, United States GPA: 3.92/4.0 Bachelor of Technology in Computer Science and Engineering 2018 - 2022 GITAM Deemed University, Andhra Pradesh, India GPA:8.85/10 CERTIFICATIONS

• AWS Certified Developer – Associate Amazon Web Services 2025

• Data Visualization with Python Coursera 2024

• Machine Learning Foundations Coursera 2024

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