About

Software quality engineering → Applied AI.

I combine 10+ years of software quality, automation, API, CI/CD, and release experience with an M.S. in Data Science, AI, and Machine Learning to build AI systems that are testable, observable, and grounded in evidence.

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

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

My path

I began in software quality, where I learned to think in edge cases, user flows, risk, evidence, and release readiness. Over 10+ years I worked across manual and automated testing, mobile and web, APIs, regression planning, defect analysis, CI/CD, and cross-functional delivery.

My graduate work added statistics, machine learning, data engineering, and responsible AI. I now apply both backgrounds to AI systems: data quality, source grounding, model behavior, prompt changes, regression detection, observability, and safety.

Current focus

Primary: Applied AI · AI Evaluation · AI Platform.

Specialty: LLM evaluation · AI quality · source grounding · data quality · regression testing.

My strongest fit is on teams that need AI systems to behave like real products: measurable, traceable, testable, and safe to change.

Recruiter snapshot

Compact proof points for Applied AI and AI Evaluation roles.

Experience

10+ years in software quality, automation, API testing, release engineering, and production reliability.

Education

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

Applied AI

AI agents, prompt governance, LLM evaluation, telemetry, source grounding, deterministic safety, and data pipelines.

Languages

English · Ukrainian · Russian.

Multilingual background supports international data validation and cross-language product research.

Project proof

Portfolio work that connects AI engineering, data science, and software-quality discipline.

World Publishing Houses

My flagship source-backed AI platform with a Research Agent, Prompt Registry, telemetry, AI evaluation, deterministic rights safety, human review, and data-engineering workflows.

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ML Model Validation

Residential property-value modeling reframed around residual behavior, limitations, and production risk rather than headline accuracy alone.

View ML case study

AI for Leaders

An applied framework for AI strategy, governance, readiness, and risk controls.

View summary

How I work

Structured, evidence-based, and practical.

Clarity before complexity

I prefer clear goals, explicit assumptions, measurable acceptance criteria, and honest tradeoffs.

Quality as a system

I look beyond individual failures to architecture, data, handoffs, observability, and release controls.

Responsible AI mindset

I separate sourced facts from model inference and design evaluation and safety boundaries around that distinction.