AI Cloud Solutions Engineer

We are seeking a Senior Applied ML Engineer to join a fast-growing team building an agent-native search platform for AI systems, the emerging web access layer for AI. You will develop and deploy machine learning models that power retrieval, ranking, and indexing at scale, helping AI systems access fresh, reliable information in real time. This is a high-impact role working on a production system used 24x7, tackling challenges comparable to large-scale web search.

Your responsibilities:

• Design, train, and deploy ML models for retrieval, reranking, and search relevance in production
• Build and optimise embedding-based indexing and large-scale retrieval systems
• Develop models supporting crawling, data selection, and content understanding
• Define and improve quality metrics for agent-native search and build evaluation pipelines
• Work on systems operating at very large scale, including high-throughput query workloads
• Collaborate closely with engineering teams to integrate ML models into production services
• Analyse performance trade-offs across latency, quality, and cost
• Experiment with and apply state-of-the-art techniques in search, retrieval, and LLM-integrated systems
• Contribute to product and architectural decisions in a fast-moving environment

Must-haves:

• 5+ years of experience in software engineering or applied machine learning
• Strong programming skills in Python, Go, or C++
• Proven experience deploying ML models in production systems
• Hands-on experience with retrieval, ranking, recommendation, or similar ML problems
• Strong understanding of machine learning and modern deep learning techniques
• Experience working with large-scale data systems and high-throughput environments
• Ability to design evaluation frameworks and define meaningful model metrics
• Product-oriented mindset with a focus on impact and iteration
• Strong problem-solving skills and ability to work in a distributed team

Nice-to-haves:

• Experience with search systems or large-scale information retrieval
• Familiarity with embeddings, transformers, and modern NLP systems
• Experience working on LLM-powered or agent-based systems
• Contributions to open-source projects, technical publications, or conference talks
• Participation in competitive ML (e.g. Kaggle) or similar signals of strong technical ability

We conduct coding interviews as part of the process.

• Build prototypes and demos across the product portfolio — serverless inference, databases, MLflow, MLOps, and vertical use cases in Physical AI and HCLS — that become assets for sales, product, and engineering teams
• Support new customers hands-on through POC design, technical onboarding, and validation; act as the bridge between their ML team and the platform during the critical first months
• Go deep on emerging applied AI — new training techniques, inference optimizations, agentic architectures, new frameworks — and turn findings into working prototypes, writeups, and product recommendations
• Feed the product roadmap with specific, grounded feedback; be the voice of "here's what broke in three customer POCs last month and here's what needs to change"
• Develop reusable technical assets — notebooks, reference architectures, benchmark results — that reduce onboarding friction at scale

• You've fine-tuned large models, debugged distributed training jobs, built production RAG or agentic pipelines, and optimized inference on GPU infrastructure — not just read about it
• You're fluent in the modern ML stack: PyTorch, HuggingFace, CUDA fundamentals, Kubernetes for ML, MLflow or equivalent, vector databases
• You've worked with enterprise ML teams — whether as a solutions engineer, customer engineer, or an ML engineer who collaborated closely with customers
• You read papers and implement them — not for credit, but because it's how you stay sharp
• You communicate with calibration: you can explain activation checkpointing tradeoffs to an ML engineer in the morning and the cost implication to a CTO in the afternoon

• Experience in any of our vertical domains: Physical AI / robotics / simulation, HCLS (drug discovery, medical imaging, clinical NLP), or enterprise AI application development
• Familiarity with MLOps at scale (Kubeflow, Metaflow, Argo, Ray)
• Prior work at a cloud provider or AI infrastructure company
• You've shared technical work publicly — notebooks, talks, blog posts that people actually use

• Competitive salary and comprehensive benefits package.
• Opportunities for professional growth within Nebius.
• Flexible working arrangements.
• A dynamic and collaborative work environment that values initiative and innovation.

#vacancy

MLOps Engineer (LLM / Multi-Agent Systems / Kubernetes / RAG) ⚡️🤖

ID вакансии: MLm_DNA

Ставка: до 250 000 руб. на руки в месяц.

Грейд: Middle+ / Senior.

Загрузка: Full-time.

Формат: Удалённо.

Локация/Гражданство: РФ.

🏢 Клиент: МТС.

🧩 О проекте: построение и развитие современных MLOps-платформ для машинного обучения с фокусом на LLM-решения, RAG-пайплайны и мультиагентные системы.

Ищем MLOps-инженера, который находится на стыке классического DevOps и передовых AI-технологий. Роль для специалиста, который не только умеет строить надежные CI/CD и оркестрировать пайплайны в Kubernetes, но и глубоко понимает специфику развертывания и обслуживания LLM, LangGraph и Multi-Agent систем.

🚀 Ключевые задачи:

— Построение, развитие и поддержка MLOps-платформ для машинного обучения. — Оркестрация ML-пайплайнов и процессов обработки данных (Apache Airflow). — Внедрение и поддержка инструментов трекинга экспериментов, версионирования данных и моделей (MLflow, ClearML, Kubeflow, DVC). — Настройка и поддержка CI/CD процессов специально для ML-моделей и сервисов. — Инфраструктурная поддержка разработки и развертывания Multi-Agent Systems, RAG-пайплайнов и LLM-приложений. — Настройка мониторинга и наблюдаемости (Observability) для ML-сервисов и LLM-инференса.

🎯 Обязательные требования (Hard Skills):

MLOps и DevOps (КРИТИЧНО!): — Глубокое понимание и опыт работы с Docker и Kubernetes (K8s). — Опыт построения CI/CD for ML (специфичные пайплайны для обучения и деплоя моделей). — Владение инструментами MLOps: MLflow, ClearML, Kubeflow, DVC.

LLM и Agentic AI (КРИТИЧНО!): — Понимание и опыт работы с фреймворками: LangChain, LangGraph, LlamaIndex. — Опыт поддержки инфраструктуры для Multi-Agent Systems и Autonomous Agents. — Понимание принципов RAG (Retrieval-Augmented Generation), MCP (Model Context Protocol), Prompt Engineering и Tool Use. — Опыт работы с API LLM-провайдеров (OpenAI и аналоги).

Оркестрация и наблюдаемость: — Уверенный опыт работы с Apache Airflow. — Опыт настройки мониторинга и наблюдаемости (Observability) для ML-систем (метрики качества моделей, дрейф данных, latency, throughput).

💡 Будет плюсом: — Опыт оптимизации инференса LLM (vLLM, TensorRT-LLM, Ollama). — Знание Python на уровне, достаточном для написания скриптов автоматизации и кастомных операторов Airflow. — Понимание векторных баз данных (Qdrant, Milvus, pgvector) и их эксплуатации.

📲 Контакт для отклика: @hrwomenpro (При отклике, пожалуйста, пишите ID вакансии: MLm_DNA)