Senior ML Engineer | Team Lead | Product Manager | AI Systems Architect
I lead cutting-edge AI initiatives at Π‘ΠΠΠ , transforming business operations through innovative machine learning solutions. With 3+ years of experience in fintech and enterprise AI, I architect scalable ML systems that drive revenue growth and operational efficiency.
Currently at Π‘ΠΠΠ : Leading a development team focused on ML solutions that directly impact company revenue through advanced algorithms including multi-armed bandit models for dynamic interest rate optimization in factoring services.
Key Focus Areas:
- π― Revenue Optimization: Multi-armed bandit algorithms for dynamic pricing strategies
- π€ AI Agents & RAG Systems: Internal employee assistance platforms with GigaChat2 & Qwen3
- ποΈ Service Architecture: Designing and implementing scalable ML service architectures
- π MCP Integration: Pioneering Model Context Protocol server implementations
- πΌ Cross-functional Leadership: Driving GenAI adoption across multiple departments
- π Direct Development: Hands-on coding and evaluation of ML services
- Strategy & Discovery: Market/competitor research, JTBD, stakeholder interviews, PRDs
- Roadmapping & Delivery: OKRs, KPI trees, prioritization (RICE/ICE/MoSCoW), backlog grooming
- Experimentation & Analytics: A/B testing, cohort analysis, growth loops, dashboards
- Go-To-Market: Problem-solution fit, pilot design, rollout plans, enablement
- LLMs & RAG: GigaChat, Qwen2.5, LangChain, FAISS, Ollama, Transformers, Prompt Engineering, RAG Optimization
- Predictive Modeling: XGBoost, CatBoost, LightGBM, Scikit-learn, NER, Recommendation Systems
- NLP: SpaCy, NLTK, Text Embeddings, Document Classification, PII Detection, Entity Extraction
- AI Agents: Autonomous document summarizers, HR policy assistants, automated data annotators
- Cloud & Orchestration: Docker, Kubernetes, GitLab CI/CD, Zero-Downtime Deployments
- ETL & Big Data: Hadoop (HDFS, YARN), Kafka, NiFi, Sqoop, Flume, RabbitMQ, HBase
- Monitoring: Grafana, Graylog, Prometheus, Logging & Alerting Pipelines
- Led teams of 5+ engineers (ML, backend, data)
- Owned product roadmaps and delivery; reduced time-to-production by 25%
- Conducted 4+ internal tech meetups (>60 attendees), 10% hiring conversion
- Translated business requirements into PRDs and technical specs (HR, Compliance, Finance)
Team Leadership & Revenue Impact:
- π― Leading ML development team focused on revenue-generating solutions
- π° Multi-armed bandit algorithms for dynamic interest rate optimization in factoring services
- π Direct revenue impact through ML-driven pricing strategies
AI Agent Platform Development:
- π€ Internal employee assistance service with multiple AI assistants
- π RAG-powered knowledge systems using GigaChat2 and Qwen3
- ποΈ Service architecture design and implementation
- π Direct development and evaluation of each service component
Innovation & Integration:
- π MCP (Model Context Protocol) server implementation across the organization
- πΌ Cross-departmental negotiations for GenAI solution adoption
- π Pioneering GenAI integration in enterprise workflows
Technical Achievements:
- Built hybrid LLM+RAG agent for HR & compliance queries (GigaChat/Qwen2.5) β Streamlit prototype β Flask production; reduced HR tickets by 60%
- Automated 70% of corporate table documentation via LLM pipeline (RabbitMQ) β hours β minutes
- Designed PII detection system β cut audit effort by 90%, saved 35M RUB/year
- Migrated 15+ services Swarm β Kubernetes β +20% uptime, zero-downtime rollouts
- Refactored legacy microservices β +45% inference speed via algorithm and caching
- Led team of 5 and Agile rollout (Jira/Confluence) β time-to-market β25%
- Scaled resume parsing 1.7K β 10K+/day (Hadoop + NiFi) β manual labeling β80%
- Deployed NER for skill extraction β MAE 12.4K β 10.2K RUB in salary prediction
- Built recommendation engine β sales conversion +37%
- Organized 4 internal tech meetups β 10% hire rate from attendees
Π‘ΠΠΠ | Role: Product Owner & ML Lead | Python, Qwen2.5, LangChain, FAISS, Flask, Kubernetes
- Defined PRD, success metrics, and rollout plan; prioritized docs ingestion and retrieval quality
- Autonomous agent answers 200+ daily HR/compliance queries over 500+ PDFs with grounded RAG
- Impact: β60% HR support tickets; CSAT +18 p.p.
Π‘ΠΠΠ | Role: Product/Tech Lead | RabbitMQ, GigaChat, Python, Pandas
- Prioritized use-cases with analysts; designed queues and SLAs
- Extracts schema/semantics from Excel/CSV and generates documentation
- Impact: 70% automation; adopted by 200+ analysts; minutes instead of hours
Π‘ΠΠΠ | Role: PM/ML | SpaCy, Rule-based NLP, ML Classifier
- Drove risk assessment with Compliance; defined precision/recall thresholds
- Detects PII (SNILS, passport, phone) across 10+ schemas; precision 98%
- Impact: Mandatory gate in pipelines; 35M RUB/year saved
Proscom | Role: Team Lead/ML | XGBoost, CatBoost, NER, Flask
- Scoped MVP with sales; iterated on matching quality via NER features
- Predicts market salary, recommends candidates; used by 500+ HR specialists monthly
- Impact: sales conversion +37%
- AI Knowledge Assistant (Enterprise RAG) β Product Owner/ML Lead. Drove PRD and rollout; achieved β60% HR tickets; stack: Qwen2.5, GigaChat, LangChain, FAISS, Flask, K8s.
- Auto-Documenter for Corporate Tables β Product/Tech Lead. Automated 70% documentation; adopted by 200+ analysts; stack: RabbitMQ, Python, Pandas, LLMs.
- PII Detection Engine β PM/ML. Mandated across pipelines; saved 35M RUB/year; stack: SpaCy, rule-based NLP, classifier.
- Resume Parsing & Salary Recommender β Team Lead/ML. Scaled 1.7K β 10K+/day; +37% sales conversion; stack: Hadoop, NiFi, NER, CatBoost/XGBoost, Flask.
Bachelor's Degree | GPA: 5.0/5.0 (Red Diploma) | 2021 β 2024
Master's Degree | Digital Product Management | 2025 β Present
I'm always open to discussions on:
- π’ Enterprise AI / RAG systems
- βοΈ MLOps & LLM scaling
- π₯ Team leadership in AI
- π€ AI ethics in finance
- π° Revenue-driven ML solutions
- π MCP server implementations
