Alexey Fateev

Key Achievements:

• Leading a cross-functional team of 15+ professionals (Data Scientists, ML Engineers, Data Engineers, System Analysts) as Tech Lead, driving technical strategy and execution across multiple AI initiatives
• Successfully delivered 5 production-ready RAG-based products and AI Agent solutions, serving the entire bank's AI infrastructure needs
• Architected and implemented from scratch an A/B testing platform for RAG products leveraging Istio Service Mesh and Argo Rollouts, enabling data-driven product optimization
• Designed and deployed canary deployment strategy from the ground up, significantly reducing production deployment risks and enabling safer rollouts
• Established unified technical and infrastructure layer across all AI products, ensuring consistency, scalability, and maintainability
• Optimized LLM model inference, resulting in a 40% performance improvement. This reduced the response time of the entire RAG service by half
• Ensured high performance and reliability of the service, maintaining SLA at 5 seconds under load of up to 250,000 requests per day
• Developed and implemented production-ready MLOps pipelines for LLM model deployment using KServe and vLLM
• Resolved infrastructure constraints by building vLLM from source with flash-attention support for legacy CUDA (11.8)
• Implemented a unified gateway (HiGress) for all LLM models and MCP (Model Context Protocol), centralizing management and access

Core Responsibilities:

• Designing architecture and participating in RAG system implementation
• Deploying and maintaining LLM inference infrastructure in new clusters based on KServe, including troubleshooting kNative and Istio components
• Client interaction: conducting meetings, developing connection schemes for new clients to RAG service, and effort estimation
• Creating unified pipelines for deploying various non-model services across multiple environments (clusters), improving release speed and consistency
• Research and implementation of best practices for optimizing and accelerating LLM model inference

• Developed and maintained a machine learning model deployment platform, managing 100+ ML models as part of a specialized ML team
• Orchestrated database operations, including table creation and structure optimization for enhanced performance
• Led critical aspects of a large-scale infrastructure migration, including server relocation and system upgrades
• Authored and implemented Lua scripts for Tarantool Cartridge cluster during application migration
• Enhanced a Golang-based database emulator for Clickhouse, improving integration testing capabilities
• Streamlined Python environment migration through RPM packaging and GitLab CI pipeline development
• Developed and deployed a chat-bot application utilizing OpenAI API, Langchain, and RAG for custom report generation
• Deployed applications in Kubernetes (k8s) environments, ensuring scalability and efficient container orchestration
• Utilized Puppet for automated server deployment and configuration management

• DWH maintenance
• Modeling new database objects from non-relational to relational form
• Implementing Grafana and Prometheus to track metrics about DAGs execution
• Creating and maintaining ETL pipelines to automate CRM interactions with customers through various communication channels (email, SMS, push notifications, etc)
• Using asynchrony to speed up query execution
• API integration with external systems

• Developed data pipelines in GCP for financial data processing, including encryption and anonymization in PCI environment
• Built backend services using FastAPI and deployed them to Cloud Run and Cloud Functions
• Created and maintained data analytics protocols, standards and documentation
• Developed web application using Django and Plotly Dash for IT job market trend analysis
• Implemented ETL pipelines using Apache Airflow for data processing
• Worked with technologies: GKE, Cloud PubSub, BigQuery, Cloud Build, PostgreSQL, Docker, Redis