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Staff AI Engineer (GenAI Platforms) | LLM Systems, Agents, Evaluation
I am a Staff AI Engineer focused on production GenAI platforms and LLM systems. I work on problems where LLMs must be reliable, measurable, and affordable in real products. My main area is LLM systems engineering: agents, dialog orchestration, evaluation pipelines, RAG, and controlled use of LLMs inside ML platforms. I build systems where LLMs are part of the architecture, not a magic black box. In my work, LLMs are used for decision support, data analysis, feature engineering, and optimization - always with offline evaluation, benchmarks, and clear metrics. I usually work close to code and system architecture, and also define technical standards and workflows for AI teams. I care about quality, latency, cost, and how systems behave under real load.
Moscow Institute of Physics and Technology (State University) (MIPT)
Master's degree, Applied Mathematics
January 1, 2001 – January 1, 2007
CybernetAI
Expert Advisor / Applied GenAI Engineer (part-time)
September 1, 2024 – Present
Tinkoff
Staff AI Engineer / Tech Lead (Search & Recsys Platforms)
August 1, 2024 – Present
Social Discovery Group
Machine Learning Tech / Team Lead
June 1, 2023 – July 1, 2024
Remote
SmartAndPoint
Independent AI Engineer (Applied GenAI Practice)
March 1, 2023 – Present
Sberbank
Principal / Lead Machine Learning Engineer
January 1, 2014 – January 1, 2023
Cultural Fit Analysis
The candidate has a diverse background working in large enterprises (Tinkoff, Sberbank) and smaller, more agile environments (CybernetAI, Social Discovery Group, SmartAndPoint). The independent AI engineering practice suggests a proactive and continuous learning mindset. The experience aligns well with a senior Data Engineer role that might involve ML pipelines and data-driven systems. However, the primary focus on AI/ML engineering rather than core data engineering (e.g., ETL, data warehousing, distributed systems fundamentals) might indicate a slight misalignment if the target role is strictly traditional data engineering.
Soft Skills & Operational Fit
The candidate's resume indicates leadership experience (Tech Lead, Team Lead) and independent practice, suggesting strong initiative and problem-solving skills. The descriptions of building standardized ML pipelines and owning platforms imply a focus on operational excellence and scalability. However, without psychometric test results, a definitive assessment of stress handling, work attitude, and team collaboration is not possible.