Resume

Resume

Applied AI and AI Evaluation engineer combining 10+ years of software quality, automation, API, CI/CD, and release experience with an M.S. in Data Science, AI, and Machine Learning from Boston University.

M.S. Data Science, AI & ML — Boston University

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Open to full-time US remote opportunities.Primary: Applied AI · AI Evaluation · AI Platform. Quality specialty: LLM evaluation · AI quality · source grounding · data quality.EmailLinkedInGitHub

Summary

Applied AI / AI Evaluation engineer and senior software-quality professional with 10+ years of experience building quality strategies, automation frameworks, release controls, and evidence-driven validation for fintech, e-commerce, web, mobile, REST APIs, data, and AI-assisted systems. Combines Python, SQL/PostgreSQL, CI/CD, AI evaluation, observability, agent workflows, and an M.S. in Data Science, AI, and Machine Learning.

Target roles

Primary focus

Applied AI · AI Evaluation · AI Platform

Strongest fit: teams building production AI systems that need evaluation, source grounding, prompt governance, observability, regression testing, data quality, and safety boundaries.

Quality specialty

LLM Evaluation · AI Quality · Data Quality · Source Grounding

Professional Experience

Full resume available as PDF.

Lead QA Automation Engineer · Data Science Contributor

Advisor360° · Weston, MA
Apr 2022 – Present

  • Design and maintain test strategies, automation frameworks, and system integration/regression coverage for complex fintech web, mobile, REST API, and backend workflows using Python, Appium, Docker, Swagger/OpenAPI, and CI/CD tooling.
  • Partner with Product, Engineering, and Business stakeholders on QA and an authorized cross-team data science assignment, translating requirements and business questions into acceptance criteria, test scenarios, analyses, KPIs, and actionable recommendations.
  • Execute SQL investigations and exploratory data analysis across enterprise PostgreSQL data to evaluate advisor behavior, customer engagement, workflow friction, anomalies, and AI-product adoption.
  • Engineer features and validate predictive models using Python, SQL, pandas, and scikit-learn.
  • Build ETL and automated data-validation workflows; collaborate with Data Engineering on data quality, schema validation, analytical data models, data integrity, and reproducible investigations.
  • Evaluate AI features using adoption, latency, inference cost, user satisfaction, and productivity metrics; communicate findings through dashboards and stakeholder presentations.
  • Investigate UI, REST API, log, database, and data-pipeline failures; isolate root causes, assess severity and regression risk, validate fixes, and support release-readiness decisions.

Founder · Applied AI / AI Platform Engineer

World Publishing Houses — Independent Project
Current

  • Build a source-backed full-stack publishing intelligence platform using Python, FastAPI, SQLAlchemy, PostgreSQL, Next.js, and TypeScript.
  • Design controlled AI research and tool-calling workflows with source grounding, safe abstention, deterministic rights-safety validation, and human review.
  • Implement prompt governance with immutable versions, hashes, evaluation-gated promotion, execution provenance, audit history, and rollback.
  • Build AI telemetry for traces, models, prompt versions, tokens, estimated cost, latency, failures, model pricing, and workspace budgets.
  • Build an evaluation platform with golden datasets, deterministic and model evaluators, regression comparison, human review, failure taxonomy, and quality gates.
  • Develop automated data-quality audits and ingestion/review workflows for multilingual publishing data, provenance, duplicate detection, and relational integrity.
View flagship case studyWatch product demo

Lead QA Automation Engineer

Peapod Digital Labs · Quincy, MA
Oct 2017 – Apr 2022

  • Led test strategy, manual and automated quality activities, defect triage, and release validation for large-scale e-commerce web and native mobile products.
  • Built Java-based Selenium and Appium frameworks and integrated regression coverage with TestNG, Maven, Bamboo, Sauce Labs, and Selenium Grid.
  • Automated functional, integration, REST API, end-to-end, cross-browser, database, performance, and production-smoke tests.

Software QA Tester

Infomatrix Global · Boulder, CO
Mar 2015 – Oct 2017

  • Built and executed Java/Selenium automated and exploratory/manual tests across the SDLC; performed functional, regression, integration, REST API, load, and performance testing with Jenkins, ReadyAPI, BlazeMeter, and JMeter.

Technical skills

Applied AI & evaluation

AI agents, tool calling, LLM evaluation, evaluation harnesses, prompt governance and regression, golden/reference datasets, source grounding, hallucination and unsupported-claim detection, deterministic guardrails, failure-mode analysis, AI telemetry, latency/cost evaluation, and human review.

Software & quality engineering

Python, Java, TypeScript/JavaScript, FastAPI, Next.js, React, pytest, Playwright, Selenium, Appium, REST Assured, Cucumber, TestNG, JUnit; test strategy, API contract validation, CI/CD quality gates, regression, integration, E2E, accessibility, performance, and release testing.

Data & platforms

SQL, PostgreSQL, SQLAlchemy, Alembic, pandas, NumPy, scikit-learn, data profiling, schema validation, ETL/ingestion validation, anomaly detection, EDA, feature engineering, predictive-model evaluation, Docker, GitHub Actions, Jenkins, Bamboo, Bitbucket, Postman, Swagger/OpenAPI.

Engineering strengths

  • Evidence-based failure analysis and root-cause investigation.
  • Regression and release gates for deterministic and nondeterministic systems.
  • Source provenance, data integrity, and reproducible investigations.
  • Cross-functional communication across Product, Engineering, QA, and Data.

Education

Boston University
M.S. in Data Science, AI, and Machine Learning

Quincy Community College
Computer Science & Programming · 2020

Agricultural University, Mykolaiv, Ukraine
Degree in Agricultural Business · 2011

Languages

English · Ukrainian · Russian.

Portfolio proof

Start with World Publishing Houses for the full Applied AI platform story, then explore the ML model-validation work.

WPH flagship case studyML validation case studyDownload PDF