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Вакансия открыта в Инновационном центре «Безопасный транспорт», который входит в структуру Московского метрополитена. С 2017 года мы собираем и анализируем Big Data всего Транспортного комплекса Москвы. Эти данные…
DevOps engineer builds and maintains CI/CD pipelines, Kubernetes clusters, and monitoring for a construction-focused AI platform, automating deployments and ensuring system reliability.
Principal Engineer to design and lead Bullish’s AI & Data Platform, building semantic layers, knowledge graphs, and conversational analytics for institutional crypto and fintech use cases.
Build and orchestrate autonomous AI agents that reason, plan, and execute workflows using Hermes Agent and MCP, integrating tools and memory systems for production-grade applications.
Build and run a secure Kubernetes platform for AI engineering tools, using GitOps and IaC to host model gateways, retrieval components, and CI/CD runners while enforcing strict data-sovereignty controls.
Develops and maintains cloud-based AI agents and RAG pipelines on Azure, mentors engineers, and ensures production stability for healthcare diagnostic platforms.
Lead Axos Bank’s AI engineering team, architecting enterprise-scale agentic systems, setting standards for LLMs and MCP integrations, and driving AI-native development across the bank.
Build and test AI agents and integrations for banking workflows using Python/JavaScript, guided by senior engineers to automate processes and improve software development.
Designs autonomous network solutions for mobile/fixed telecoms using AI frameworks (Vertex AI, LangGraph), network programmability, and agentic systems to optimize performance and cost.
Build and maintain the AI platform and data lakehouse, automating operations and deploying services using Python, Kubernetes, and MLOps tools.
Build production-grade AI systems and data pipelines end-to-end, owning schema design, ETL, FastAPI/Next.js apps, and cloud deployments on AWS/Azure.
Build and deploy LLM-powered tools, RAG systems, and agentic workflows, then run the Kubernetes-based AI infrastructure that serves them reliably across the company.
Build AI-powered tools to accelerate hardware design and testing workflows using Python, TypeScript, and vector databases.
Build and own the backend for real-time AI assistants used in malls, airports, and stadiums, using Python, Django, PostgreSQL, and GCP.
Build and deploy LLM-powered automation agents for pharma workflows using Python/JavaScript, PostgreSQL, vector DBs, and n8n on an NVIDIA DGX Spark AI sandbox.
Build production-grade AI systems like copilots, RAG, and agents using LLMs and vector databases, deploying them in enterprise environments.
Build end-to-end web apps with .NET Core back-end and Vue/React/Angular front-end, deploy AI features to production, and lead cloud-native projects on Azure or AWS.
Build and maintain the AI platform and data lakehouse, automating operations and integrating services with APIs and cloud infrastructure using Python, Kubernetes, and MLOps tools.
Build and deploy enterprise-grade LLM applications using RAG, AI Agents, and vector databases like Milvus/Qdrant. Develop Python-based AI workflows and APIs for real-world production use.
Builds and maintains full-stack web apps while integrating AI/Generative AI models to enhance user experiences and functionality.
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