Lead System Analyst for the Anti-Fraud Solutions Team

What you will be doing

• Developing platform architecture together with the architect and the team
• Shaping the product vision together with the PO
• Communicating with clients, guiding them, managing expectations
• Breaking down and estimating tasks (together with the team), being responsible for deadlines and quality of solutions
• Developing technical specifications, aligning requirements between DS and MLOps teams and clients
• Developing/describing solutions for pipelines and integrations with LLMs
• Creating and maintaining documentation for user tools for working within a unified ML environment for application and training
• Scaling developed systems and tools for managing the lifecycle of machine learning models

Our requirements for candidates

• 3+ years of experience in developing infrastructure and/or microservice solutions
• 1+ year of experience with ML/MLOps
• Good understanding of containerization, orchestration, CI/CD
• Experience with Hadoop, Spark, Kafka, ELK, SQL
• Understanding of RAG and LLM principles, AI agents
• Independence, proactivity, and initiative
• Full immersion in the infrastructure and team, readiness to help colleagues and users
• Ability to grasp the overall picture of the expected result, rather than solving a specific given task

What we offer

• Stable and transparent income: salary is discussed after the interview
• Work in an agile team
• Office-based or hybrid work format (your choice)
• Complex and interesting tasks, modern technology stack
• Opportunities for vertical and horizontal career growth: regular training, webinars, meetups, and demo days
• Access to free corporate libraries Alpina Digital, MyBook, and business publications
• Exclusive offers from the Bank for employees: own gym, as well as discounts on services from travel agencies, food products, in restaurants, bars, and shops
• Working for the thrice-best employer in the Russian Federation according to hh.ru, Forbes, RBC
• AI literacy development program: tracks for working with neural networks from basic to advanced level