AI engineer

Мы в поиске Senior AI инженера в международную компанию. О вакансии Компания создает слой искусственного интеллекта на своей платформе: встроенный в приложение помощник, который отвечает на вопросы и выполняет действия, аналитические помощники на базе нашего хранилища данных, сервер MCP, предоставляющий более 100 платформенных инструментов для помощников с искусственным интеллектом, и мультиагентные системы, автоматизирующие диспетчеризацию. В качестве старшего инженера по искусственному интеллекту вы будете проектировать, внедрять и эксплуатировать эти системы в производственной среде.

Стек — Python на Google Cloud (Vertex AI, Cloud Run, Big Query) с несколькими поставщиками моделей (Gemini, Claude).

Build and own the in-app AI copilot that answers questions and performs actions on routes, orders, drivers and vehicles;
Extend our MCP server (100+ tools) so AI assistants can work with the platform safely;
Design multi-agent systems for automated dispatching, with agent routing and human approval for risky actions;
Build AI analytics: natural-language questions and insight reports over our Big Query data;
Own the AI platform layer: prompt and model versioning, model routing, caching, guardrails, tracing, cost and latency;
Write design docs and ADRs; work with the PO, backend (PHP/Python) and frontend teams; share AI practices across the team;

5+ years of commercial software engineering, Python as the primary language (async, typing, testing; FastAPI or similar); 2+ years building and shipping LLM-powered products to production for real users; MCP servers: design and build MCP servers and tools on top of existing REST APIs (auth, multi-tenancy, rate limits, error handling); AI chats: production conversational assistants with multi-turn context, streaming, tool calling and structured outputs;
Agents: tool-using agents with planning, state, retries, termination conditions and human-in-the-loop for risky actions;
Agent systems: multi-agent orchestration (supervisor/worker, handoffs, shared state) with Lang Graph, Google ADK, OpenAI Agents SDK or custom code;
Agent routing: intent classification and routing between agents, tools and models by task, cost and latency, with fallbacks; RAG: chunking, embeddings, vector and hybrid search, reranking, grounded answers with citations;
System prompting and context engineering: versioned system prompts, tool descriptions, few-shot examples, context window management, prompt-injection defense;
Modern AI architecture: model abstraction across providers (Gemini, Claude, OpenAI), caching, guardrails, tracing and observability, cost and latency control;
Cloud and delivery: GCP or AWS, Docker, CI/CD (Git Hub Actions), monitoring;
Security for AI actions: tenant isolation, least-privilege tools, audit logs; AI-assisted development as a daily habit (Claude Code, Cursor, Copilot);
English B2+ (written and spoken); clear design docs and ADRs; Будет плюсом:
Production evals: golden datasets, CI regression gates, LLM-as-judge, A/B tests of prompts and models;
Google Cloud:
Vertex AI (Gemini, Agent Engine, prompt management, context caching), Cloud Run, Big Query, Pub/Sub;
Text-to-SQL and analytics agents over Big Query-scale data;
Workflow engines:
Google Workflows, Temporal, or event-driven pipelines (Pub/Sub, Kafka);
Long-term (semantic) memory for agents, scoped per tenant;
Frontend for AI UX:
Type Script with Vue or React/Next.js (streaming chat UIs);
Kubernetes (GKE), Helm, Terraform;
Ability to read PHP/Laravel code (our core platform APIs);
Fine-tuning or serving open-weights models (Gemma, Llama, Qwen; vLLM);
Optimization tools: OR-Tools, VRP heuristics;
Published MCP servers, connectors or open-source work in AI tooling

Semi-flexible working hours;
Completely remote work;
Interesting projects to work on;
Exposure to a variety of industries and technologies.