Machine Learning Engineer - AWS SageMaker, NLP

Anmol Raju Deshmukh

Naperville, IL - ***** 302-***-**** *****.**********@*****.***

LinkedIn: https://www.linkedin.com/in/anmol-deshmukh-101/

PROFESSIONAL SUMMARY

Machine Learning Engineer with 3 years of experience delivering high-impact AI solutions across finance,

healthcare, and academic domains, with a strong focus on automation and real-time analytics. Proven ability to

design, develop, and deploy end-to-end machine learning systems that improve accuracy, reduce latency, and

enable data-driven decision-making. Skilled in collaborating with cross-functional teams to translate complex

business requirements into scalable, production-ready solutions, while continuously enhancing model performance

through innovation and intelligent automation.

TECHNICAL SKILLS

Languages & Databases: Python, Java, R, C++, PySpark, MySQL, NoSQL, PostgreSQL, MongoDB, Cassandra Machine.

Learning: Supervised & Unsupervised Learning, Classification, Regression, Clustering, Time Series.

Deep Learning & NLP: Neural Networks, CNN, RNN, LSTM, Autoencoders, Text Preprocessing, Word Embeddings.

Framework & Libraries: Pandas, NumPy, Dask, Spark, TensorFlow, Keras, PyTorch, Scikit-learn, OpenCV.

Data Visualization: Matplotlib, Seaborn, Plotly, Tableau, Power BI.

Big Data & Cloud: Apache Spark, Hadoop, Hive, AWS (S3, EC2, SageMaker), Google Cloud Platform, Azure.

Model Deployment: Flask, FastAPI, Docker, Kubernetes, MLOps, MLFlow, AWS SageMaker, Google Cloud AI.

Other Tools: Jupyter Notebook, PyCharm, Visual Studio Code, GitHub, Docker, Jenkins.

Techniques: Probability, Hypothesis Testing, Statistics, Mathematics, EDA, Predictive modeling.

PROFESSIONAL EXPERIENCE

T-Mobile, KS June 2024 – Present

Machine Learning Engineer

• Scaled end-to-end ML pipelines on AWS SageMaker, automating feature engineering and model training for

1M+ daily records, reducing manual intervention by 40%.

• Optimized Big Data workflows by leveraging PySpark and Snowflake, improving data retrieval speeds by

35% and enabling real-time analytics for stakeholders.

• Implemented robust MLOps practices using MLflow and Jenkins CI/CD, cutting the model deployment

lifecycle from weeks to days while ensuring 99.9% production reliability.

• Performed extensive data analysis using Python, Pandas, NumPy, and visualization tools like Matplotlib and

Seaborn.

• Developed models using Scikit-learn, TensorFlow, and PyTorch, ensuring high performance and scalability.

• Conducted feature engineering and selection to enhance model accuracy and reduce overfitting.

• Applied hyperparameter tuning techniques such as Grid Search and Random Search for model optimization.

• Built and managed data pipelines using SQL, Apache Spark, and Hadoop for large-scale data processing.

• Deployed machine learning models into production using Docker, Kubernetes, and cloud platforms like AWS

(SageMaker, EC2, S3), Azure ML, and GCP.

• Developed RESTful APIs using Flask/FastAPI and Docker to integrate ML models into business applications.

• Worked with version control and collaboration tools such as Git, GitHub, and Bitbucket.

• Utilized MLflow, DVC, or similar tools for experiment tracking and model versioning.

• Monitored model performance using metrics and implemented retraining pipelines for continuous

improvement.

• Automated workflows using CI/CD tools like Jenkins and GitHub Actions for seamless deployment.

• Applied statistical techniques and probability concepts to validate model assumptions and outcomes.

• Ensured data quality, governance, and security while handling structured and unstructured datasets.

• Pioneered Generative AI initiatives, exploring RAG (Retrieval-Augmented Generation) and LLM

finetuning to enhance internal knowledge retrieval systems.

• Worked on cloud-based data warehousing solutions such as Snowflake, Redshift, or BigQuery.

• Demonstrated strong understanding of NLP/Computer Vision using libraries like NLTK, SpaCy, or OpenCV.

• Continuously explored and implemented new AI/ML advancements to enhance model performance and

business impact.

Onward Technologies Inc, India July 2022 – June 2023

Software Engineer - ML

• Designed and developed a Python-based Image Comparison System leveraging Machine Learning (ML)

techniques for accurate detection and quantification of visual differences across documents and images.

• Built and deployed a production-grade solution using OpenCV, NumPy, and scikit-learn, ensuring scalability,

performance optimization, and system robustness.

• Incorporated advanced similarity techniques including Structural Similarity Index (SSIM) and featurebased

matching algorithms to enhance model accuracy and visual comparison precision.

• Applied key Image Processing and Pre-processing techniques such as normalization, noise reduction,

image alignment, and transformation to improve model performance.

• Integrated Deep Learning-based feature extraction using pre-trained CNN architectures (Convolutional

Neural Networks) to improve representation learning and comparison accuracy.

• Developed and optimized end-to-end Machine Learning pipelines, including data ingestion,

preprocessing, feature engineering, model inference, and deployment workflows.

• Implemented computer vision-based visualization techniques for side-by-side difference highlighting,

improving model interpretability and user experience.

• Engineered robust model evaluation frameworks using standard ML metrics including Accuracy, Precision,

Recall, and F1-score for performance validation.

• Performed hyper parameter tuning and model optimization to improve generalization and achieve higher

performance across diverse datasets.

• Utilized Deep Learning frameworks such as Tensor Flow and PyTorch for building, training, and

experimenting with image analysis models.

• Automated large-scale batch processing pipelines, enabling efficient handling of high-volume image

comparison workloads.

• Used Git (Version Control) and experiment tracking tools to ensure reproducibility, collaboration, and

model version management.

• Implemented logging, monitoring, and error-handling mechanisms to ensure production reliability,

debugging efficiency, and system observability.

• Collaborated with cross-functional teams to translate business requirements into scalable AI/ML solutions

aligned with product goals.

Key Achievements

• Optimized machine learning models and end-to-end pipelines, achieving a 25% reduction in processing time

across multiple workflows through performance tuning and efficient resource utilization.

• Successfully designed and delivered AI/ML solutions presented at prestigious industry forums, including the

MORS Symposium and Medical Device Summit, demonstrating strong technical credibility and innovation.

• Demonstrated advanced expertise in Generative AI and Large Language Models (LLMs), with hands-on

experience in developing and deploying real-world ML solutions.

EDUCATION

• Master of Science in Computer Science - Arizona State University - May’2025

• Bachelor of Engineering in Computer Engineering - Savitribai Phule Pune University, India - July’2023

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