Hi, I am Ilya!
I help AI products go from “it works in the demo” to “it works for real users.”
Nice to meet you!
About me
Ilya Garaeff
AI Product Manager | RLHF, Eval & Multilingual AI Systems
I decide what “good” looks like for AI models, then build the systems to prove it — smarter, safer, culturally fluent.
As an MBA-educated strategist and CAPM-certified operator, I specialize in translating frontier AI capabilities into products people can trust. I sit at the intersection of model behavior and user needs — turning ambiguous, high-stakes technical requirements into clear priorities and shippable outcomes. My work ensures that AI products aren’t just powerful, but reliable enough to put in front of real users.
My approach is rooted in product judgment under ambiguity: I don’t just manage tasks, I define what “good” looks like for an AI model and build the frameworks — RLHF pipelines, evaluation systems, multilingual QA — that get it there. Whether I’m setting quality bars for LLM outputs or leading cross-functional teams to close alignment gaps across global markets, my focus is the same: fewer false signals, faster decisions, models that actually work for the people using them.
Fluent in English, Spanish, and Russian, with Mandarin at HSK5 and Portuguese in active progress, I treat linguistic and cultural nuance as a product requirement, not an edge case. I’m driven by the challenge of turning complex, technical, often-messy AI roadmaps into decisions that ship — and that hold up once real users get their hands on them.
Portfolio
Multilingual RLHF & Model Readiness
Turing
Voice AI Evaluation & Benchmarking
micro1
Website Localization for Global Product-Market Fit
Translation Commons
E-Learning Product Quality & Learner Outcomes
AYP Business School
Mentoring Taxonomy & Program Design
Translation Commons
Skills
Product & AI Strategy
Model Evaluation & Readiness Decisions: I design the QA frameworks that answer the question every AI team eventually faces — is this model good enough to ship? By structuring evaluation criteria and reducing semantic drift, I turn subjective model behavior into a clear go/no-go signal for product and engineering leadership.
Data Strategy & Pipeline Prioritization: I manage high-velocity annotation pipelines with a product lens — deciding which data actually moves model quality, not just which data is easiest to collect. The result: higher-fidelity training inputs and fewer wasted engineering cycles.
Agile for AI Product Development: I adapt Scrum/Kanban to the reality of AI development, where “done” is fuzzy and requirements shift as models learn. My focus is resolving the tension between engineering velocity and data quality — so teams ship faster without shipping worse.
🧰 The AI Product Toolstack
Evaluation & RLHF Platforms: Direct hands-on experience across Turing, Mercor, and OpenTrain AI — benchmarking model outputs, writing structured evaluation criteria, and producing 1,000+ multilingual responses across medical, legal, financial, and technical domains. This is where I learned what “model quality” actually means in practice, not theory.
AI Tools Implementation: Led client-facing rollouts of NotebookLM, ChatGPT Team, and Gemini API — translating AI capability into workflows non-technical teams could actually adopt. The product question was never “does the model work,” it was “will people use it.”
Data & Analysis (in progress): Building SQL and Python/Pandas fluency to move from qualitative eval judgment to quantitative product analysis — closing the gap between “this output looks wrong” and “here’s the metric that proves it.”
AI Product Strategy
Roadmap & Process Optimization: Auditing data pipelines end-to-end to find where quality actually breaks down — then prioritizing the lean fixes that shorten the feedback loop between human evaluators and ML engineers, so teams iterate faster on what matters.
Model Quality & Product Impact: Focused on outcomes that move the needle — translating qualitative linguistic insight into the quantitative metrics that tell you whether a model is actually improving, and whether it’s ready for the next stage of the roadmap.
Cross-Functional Product Leadership: The connective tissue between ML Research, Product Design, and Executive Leadership — turning technical constraints into product tradeoffs, and product priorities into technical requirements everyone can act on.
🌍 Multilingual Product Development
Multilingual Semantic Alignment: Fluent in English, Russian, and Spanish, with Mandarin at HSK5 and Portuguese in active progress — I use this to catch where AI models lose intent or cultural safety across languages before users do, turning linguistic nuance into a product requirement rather than a QA afterthought.
Global Product Coordination: Proven track record coordinating cross-functional teams across the Americas, Europe, and Asia to ship AI products that actually work for non-English-first users — not just translated, but culturally intelligent by design.
Linguistic Taxonomy & Data Strategy: Leading SME teams to define the taxonomies that shape non-English model training — deciding what “correct” looks like in a given language and market, then building that decision into the data pipeline.
Testimonials
Ilya brings a strong eagerness to learn, a genuine passion for product management, and a commitment to professional development that is evident in how he approaches his work.
Soeren Eberhardt
Localization Director, Translation Commons
His reliability, attention to detail, and strong sense of prioritization make him a trusted partner on complex initiatives.
Kunal Krishn
Founder, ConnectLocally
I highly recommend Ilya for his professionalism, proactive attitude, team-oriented approach, and readiness to embrace new challenges. He is not only capable but also a pleasure to work with, bringing both insight and energy to every project he undertakes.
Luz Valdez
Executive Director, AYP Business School