Machine Learning Engineer

MEL SILVERBECK

*************@*****.*** https://linkedin.com/in/mel-silverbeck 347-***-****

Profile

Results-driven Senior Machine Learning Engineer with 7+ years of experience in designing, deploying, and optimizing AI solutions on AWS Cloud. Proficient in Python and specialized in developing and fine-tuning Large Language Models (LLMs) to drive impactful business results; currently seeking exciting new opportunities in the AI/ML domain.

Technical Skills

Machine Learning & AI:

Supervised & Unsupervised Learning, Reinforcement Learning, Deep Learning

Model Deployment, Transfer Learning, AutoML

Large Language Models (LLMs) & NLP:

Fine-tuning, Optimization & Deployment of LLMs (e.g., Bedrock, Llama, GPT variants)

Text Classification, Sentiment Analysis, Topic Modeling, Named Entity Recognition (NER)

Expertise in designing scalable pipelines for LLM inference and conversational AI applications

MLOps & Deployment:

Docker, MLflow, CI/CD (Jenkins, GitLab CI)

Model Monitoring & Logging

Frameworks & Libraries:

TensorFlow, PyTorch, Keras, Scikit-Learn, Hugging Face Transformers, Pandas, NumPy, Dask

Programming & Scripting:

Python, SQL

AWS Cloud:

SageMaker, EC2, Lambda, S3, Redshift, Athena, Step Functions, EventBridge, CloudWatch, IAM, DynamoDB, Glue, CloudFormation

API Development:

Designing and building RESTful/asynchronous APIs using FastAPI

Mathematics & Statistics:

Probability, Linear Algebra, Optimization, Statistical Analysis

Professional Experience

Senior Machine Learning Engineer JP Morgan Chase 08/2021 – Present Jersey City, NJ

Spearhead the development of cutting-edge machine learning and deep learning solutions, leveraging NLP techniques to drive personalized marketing strategies and enhance customer interactions.

Lead the deployment of scalable ML models using AWS services, including S3, EC2, and SageMaker, ensuring efficient and reliable production pipelines.

Collaborate cross-functionally with data scientists, engineers, and product teams to integrate ML-driven solutions, contributing to a 30% increase in customer engagement.

Design, develop, and fine-tune LLM-based transformers for key NLP tasks, including classification, sentiment analysis, and text summarization.

Optimize data workflows, reducing model training time by 40% through efficient preprocessing techniques using Pandas and NumPy.

Leverage Apache Spark on AWS to build scalable data pipelines, improving model accuracy and inference performance.

Implement robust MLOps practices using MLflow, automating model monitoring, logging (Prometheus), and updates for enhanced model reliability.

Utilize Docker for containerization and deployment of ML models, ensuring scalability and seamless integration into production environments.

Establish and maintain CI/CD pipelines for continuous integration and deployment of ML models, leveraging Jenkins for streamlined automation.

Partner with stakeholders to translate business challenges into data-driven AI solutions, aligning ML strategies with organizational goals.

Mentor and guide junior engineers, fostering technical skill development and promoting best practices in AI/ML engineering.

Machine Learning Engineer Moody's 02/2020 – 07/2021

New York, NY

Built and refined predictive models, leading to a 20% increase in the accuracy of customer identity verification processes.

Utilized AWS services, including SageMaker, Lambda, and Glue, for efficient model development and deployment to enhance KYC compliance.

Created predictive analytics for risk scoring and fraud detection, improving customer onboarding and due diligence.

Conducted data analysis using clustering, classification, and Topic Modeling to segment customers for better risk profiling.

Partnered with IT teams to integrate machine learning models seamlessly into the KYC workflow and compliance systems.

Managed CI/CD pipelines for continuous model deployment and monitoring using GitLab CI.

Utilized SQL and Python for data analysis and feature engineering.

Data Scientist Regulatory Data Corp 02/2018 – 01/2020

King of Prussia, PA

Developed ML models for customer identity verification and fraud detection, improving compliance accuracy by leveraging structured and unstructured data.

Conducted exploratory data analysis (EDA) using Pandas, Dask, and SQL to identify trends and patterns in financial transaction data for risk profiling.

Engineered features from large-scale datasets using Spark and Hadoop, optimizing model performance for KYC-related classification tasks.

Built predictive analytics solutions to assess customer risk and enhance due diligence processes, reducing false positives in fraud detection.

Collaborated with data engineers to ensure the availability of high-quality, well-structured data for ML model training and inference.

Developed statistical models and scoring algorithms to automate risk assessment, improving the efficiency of compliance workflows.

Created data visualization dashboards using AWS services to track KYC metrics, providing actionable insights to stakeholders.

Education

MA International Business Management

Cesar Ritz Colleges – Brig, Switzerland 01/2011 – 05/2012

Certifications

AWS Certified Solutions Architect – Associate

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