2017Interface foundations

I began with interfaces: turning user needs into clear, usable products. That product perspective became the starting point for my work in machine learning.

2019Training and serving models

30% lower average customer-support response times: the result of the attention-based TensorFlow encoder–decoder I built for airline customer service. I served it through a Flask API and automated scheduled retraining on Azure, connecting model development, serving and maintenance.

2020Predictive ML

Savings equivalent to six months of support operating costs were supported by my outlier-detection models, which identified critical operational issues. I combined ETL pipelines and dashboards with conversation and agent classification, churn detection and time-series revenue forecasting.

2021Services and integrations

Higher customer satisfaction was supported by the CRM workflow improvements I delivered. I built Python, Go and Node.js microservices, connected them through Angular and supplied data for operational KPIs, extending my work from analytics to the applications around it.

2022Features for ML

20% lower operating costs across the ML platform engagement were supported by my Kubernetes cost and utilization dashboards.

From Oct 2021 to Feb 2023, I built a centralized feature store and expanded a self-service UI for platform microservices. Load and latency tests, deployment improvements and on-call incident response helped teams bring applications into production.

2023ML platform operations

From Feb 2023 to Apr 2025, my focus shifted to operating an ML platform. I maintained Kubernetes infrastructure and containerized services, including Java services, deployed with Helm and GitLab CI/CD, and automated infrastructure with Terraform, Python, Go and Bash.

Monitoring with Splunk and New Relic, resolving incidents and documenting operational runbooks were part of keeping ML applications running in production.

2024Data pipelines and migration

From Sep 2024 to Apr 2025, alongside the ML platform engagement, I supported an analytical data migration. I built ETL workflows and Airflow DAGs, automated data operations with Python and shell, and optimized SQL across extraction, transformation and loading. Monitoring and troubleshooting supported performance and availability; GitLab supported collaborative delivery.

2025Reliable payments

I applied the same production discipline to payment services in Python, Java and PHP for subscriptions, invoices and refunds. I integrated Stripe, Zuora and 3-D Secure for on-session and off-session flows, built Spanner migrations and restricted card storage to reduce card-testing fraud.

2026Memory and retrieval

The short-term memory initiative I contributed to lifted conversion rate and revenue per visitor with statistical significance, an estimated USD 8.4M in annualized incremental revenue, and was recommended to scale to more users. I build Databricks and PySpark pipelines that give a shopping assistant persistent context, turning conversations and clicks into structured memories in PostgreSQL and pgvector, with deduplication, expiry, trimming and checkpoint recovery.

Production benchmarks guide embedding dimensions, retrieval quality, storage and LLM cost. I integrate Azure OpenAI and LiteLLM routing, orchestrate batches with Azure Data Factory, work with Java services and deploy to AKS with Argo CD. Structured logs, dashboards and production traces support monitoring and troubleshooting.