Jose A.

Collaborating with Data Scientists to design and building foundational software components for an industry-leading NLP library;

Improving NLP model inference speed by 20% by implementing JIT compilation and Quantization for a wide spectrum of models;

Improving NLP model training speed by 10% by optimizing the code, implementing JIT compilation, and switching custom functionality for faster standard libraries;

Recovering approximately $10k/month (~9%) from cloud costs by optimizing the testing strategy and reducing the memory footprint of models during testing;

Designing and rolling out an automated release pipeline that reduced the effort of code contributors.

Designing and building a microservice architecture that leveraged machine learning to wrangle a server farm in an automated fashion, eliminating the need for human intervention and increasing task throughput by 40%;

Designing and building an event-driven ETL pipeline which program managers use to monitor the status of tasks and interactions with customers;

Identifing implementation strategies for disk optimization in servers that lead to savings of 20% of storage costs. Such methods included optimization of storage of log files, smart deletion of versioned files, compression and archival of infrequently accessed resources, and migration of data to more cost-effective locations;

Training and coaching of personnel with zero technical knowledge to become full- time developers.

Building and deploying a scalable and maintainable mission-critical ETL pipeline, transforming data from multiple POS systems into the company's data warehouse;

Deploying the configuration of various production systems to Manage AWS using services like EC2 and S3;

Creating Unit Tests for production components written in Python;

Troubleshooting defects in production code written in Python.

Architecting ETL jobs to enable accurate billing of multimillion-dollar contracts by comparing multiple sources and finding variances and gaps;

Creating Unit Tests for production components;

Managing operation and deployment of microservices and applications;

Creating asynchronous APIs;

Implementing multiprocessing + asynchronous functionality in the existing code base to submit large quantities of events in parallel to Kafka streams, increasing performance by 10x. Providing mentoring to Jr. Python Developers working on the IBM Managed Apps division. Participating in the technical interviews of Mid-Level Back End developer candidates for the MCMP division

Leading the BI services division on one of the company's largest accounts;

Managing all aspects of software development, data engineering, and reporting;

Architecting a scalable data pipeline that handled hundreds of millions of transactions per-day by leveraging existing data sources;

Performing data analysis;

Building interactive reporting dashboards for non-technical users;

Automating ETL Tasks;

Designing and conducted yearly travel program reviews for senior leadership Coached Jr Business Analysts.

Building data scalable transformation scripts;

Managing on-cloud storage of data;

Implementing forecast functionality by implementing classification and regression algorithms in Python Performing data analysis with R and Python (Jupyter Notebooks);

Building interactive reporting dashboards for non-technical users;

Creating and optimized SQL and NoSQL queries for consuming big data from databases.