科技職缺
職缺列表
Senior Automation QA Engineer (Python)/ Инженер по тестированию
Senior QA automation engineer building and maintaining a Python test framework for an AI-powered copilot/chatbot/agent platform, automating backend and frontend checks with Playwright, pytest, and asyncio.
Аналитик данных
In-house data/CRM analyst at a Moscow HoReCa (hospitality/spa) business, segmenting guest databases, configuring a CRM from scratch, pulling/transforming large datasets from 1C, running email/SMS/push campaigns, and running A/B tests to improve LTV, retention, and churn in Moscow.
Автотестировщик в крауд-тестирование
Remote automation tester for Yandex Crowd's crowd-testing platform: develop, maintain, and analyze automated tests using JavaScript/TypeScript or Python, work with DevTools and CI/CD, and support releases alongside QA and dev teams.
Senior/Senior+ ML Engineer (DLP)
Senior ML Engineer building and maintaining production ML services for a corporate Data Loss Prevention (DLP) cybersecurity product. Day-to-day work spans CV/NLP model development, LLM inference optimization, and end-to-end ML pipeline ownership using Python, PyTorch, ONNX, Triton, and vLLM.
Senior QA Engineer
Senior remote QA engineer manually testing high-load backend services (PHP, Node.js) and REST APIs, maintaining cases in Qase TMS, running Grafana K6 load tests, triaging logs in Grafana, and collaborating with product, DevOps, and developers.
AI Agent Engineer (NLP/LLM)
Builds multi-agent AI systems and RAG pipelines on LangGraph/FastAPI for an electrical-equipment manufacturer's corporate stack, integrating LLMs with enterprise systems (1C, Bitrix, CRM, WMS) and packaging solutions as microservices.
Senior ML Engineer
Senior ML Engineer on Sber's text-to-speech team, training LLM-based TTS, voice cloning, and speech2speech generative models using reinforcement learning. Core tech: TTS, LLM, CV, RL (PPO/GRPO/DPO/RLHF).
AI & Data Solutions Architect
Architect scalable AI and Data solutions — predictive models, generative AI tools, recommendation engines — while guiding integration with enterprise platforms and moving R&D into production via CI/CD, Python, TensorFlow, PyTorch, and Kubernetes.