Разработка вакансии

🔎 Maqsad: Python da telegram bot, rest api loyihalar qilib sota olish va git, git hub, docker rabbit texnologiyalarni o'rgata olaman.

Вам предстоит делать сервис дружелюбнее к агентскому миру — как с точки зрения пользовательских сценариев, так и в глубоко техническом плане. • Разрабатывали на Go.

Location: Europe Workplace: Remote ABOUT ENEBA At Eneba, we’re building an open, safe and sustainable marketplace for the gamers of today and tomorrow. Our marketplace supports close to 20m+ active users (and growing fast!), provides a level of trust, safety and market accessibility unparalleled to none. We’re proud of what we’ve accomplished in such a short time and look forward to sharing this journey with you. Join us as we continue to scale, diversify our portfolio, and grow with the evolving community of gamers. ABOUT THE ROLE We’re looking for an Analytics Engineering Lead to set the direction for analytics engineering at Eneba and help the team deliver trusted, business-ready data. You’ll lead a team of around 10 analytics engineers. You’ll define what analytics engineering owns and where it is going, connect the team’s work to real business needs, and ensure curated data can be used directly by business teams, BI tools, and AI agents without an analyst in between. WHAT YOU’LL DO - Create and own the analytics engineering strategy, direction, and goals. - Lead, coach, and develop a team of around 10 analytics engineers through regular feedback, one-to-ones, performance management, review cycles, and hiring, helping them become increasingly autonomous. - Establish the analytics data-modeling architecture, engineering principles, quality standards, and ways of working that guide how the team builds and maintains models. - Ensure analytics engineering work begins with a clear understanding of the business problem and delivers data that people can use with confidence. - Enable reliable self-service analytics by making curated data usable by business teams, BI tools, and AI agents, with appropriate semantic layers, metrics, discoverability, and governance. - Evaluate the current analytics landscape and make pragmatic, evidence-based decisions about what should be improved, stopped, rebuilt, or delegated. - Partner directly with stakeholders across the company, including teams such as Accounting, Payments, and Marketing, as well as senior leaders. - Build a strong understanding of Eneba’s business and translate varied stakeholder needs into a clear direction for the team. - Identify and empower technical experts within the existing team to help define and implement engineering standards. - Evaluate analytics tooling and delegate technical discovery where appropriate, while retaining final ownership of analytics engineering architecture and tooling decisions. REQUIREMENTS - Proven experience leading and developing data, analytics, or analytics engineering and people-management in product-focused tech companies. - Experience defining strategy and setting direction for analytics engineering, data modeling, or a closely related function. - A track record of establishing data-modeling architecture, principles, or standards that guide how other engineers build and maintain models. - Strong business judgment: you focus on why data is needed and what problem it solves, not only how models are built. - Strong stakeholder management and communication skills, with proven experience partnering closely with product leadership and representing data in executive-level and cross-functional discussions. - Ability to form clear, evidence-based opinions, challenge existing approaches, and lead change under sustained pressure including recommending that work be stopped or rebuilt when needed. - Experience operating in a high-growth, fast-moving organization with low approval layers, where data supports many business functions. Scale-up experience is helpful. - Working knowledge of SQL and data modeling, hands-on experience with DBT, and familiarity with at least one BI or dashboarding tool. - Fluent English (business proficiency required) What it’s like to work at Eneba - Opportunity to join our Employee Stock Options program. - Opportunity to help scale a unique product. - Various bonus systems: performance-based, referral, additional paid leave, personal learning budget. - Paid volunteering opportunities. - Work location of your choice: office, remote, opportunity to work and travel. - Personal and professional growth at an exponential rate supported by well-defined feedback and promotion processes. - We’re an international team and our business language of choice is English. Good English level is required, proficiency is preferred. Please submit your resume in English - To find out about how we handle your personal data, make sure to check out our Candidate Privacy Notice https://www.eneba.com/candidate-privacy-notice   #LI-LK1

Задачи: — Дизайн и построение LLM пайплайнов, AI агентов и RAG-систем с функциями вызова инструментов и структурированным выводом (JSON schema) — Разработка production-grade Python сервисов с использованием Django, Celery, Redis — Валидация продуктовых гипотез на основе реальных данных и аналитики — Измерение и оптимизация качества, производительности и стоимости LLM — Тесное сотрудничество с продакт-менеджерами и инженерами для превращения идей в масштабируемые решения Требования: — Опыт создания production LLM приложений — Практические знания RAG, embeddings, векторного поиска, prompt engineering и оценки LLM — Опыт работы с LangGraph, LangChain или аналогичными фреймворками — Сильный опыт Python backend (Django, PostgreSQL, асинхронное программирование) — Навыки работы с SQL и Pandas, ориентированность на данные — Владение английским языком на уровне B2-C1, русским на уровне C1 — Локация: любой город, кроме России и Беларуси Будет плюсом: — Опыт работы с векторными базами данных (Qdrant, pgvector и др.) — Опыт дообучения, классификации текста или работы с embedding моделями — Опыт с Kubernetes, gRPC или Go Условия: — Удалённая работа — Гибкий график — Профессиональное развитие Компания Mad Devs занимается решением сложных задач и созданием инновационных решений в области AI, проекты охватывают Европу, США и Юго-Восточную Азию. Вакансия направлена на революцию в юридической индустрии с помощью AI.

-Разработка новых модулей и развитие существующих на Java/Kotlin. -Проведение code review коллегам.

🔎 Maqsad: Android dasturlashni o'rganmoqchi bo'lganlarga imkonyat boricha o'rgataman.

-Проектировать и разрабатывать асинхронные, отказоустойчивые сервисы на Scala.

-Масштабирование и повышение доступности системы. -Необходимо обеспечить стабильную работу системы распределённого трассирования в условиях роста нагрузки.

-Уверенные знания синтаксиса языка Go.

-Внутреннее внедрение CRM системы в качестве разработчика. -Разработка, доработка и создание интеграционных модулей в рамках CRM-системы.

-Программирование в СУБД Oracle, подготовка API (SQL, PL/SQL). -Оптимизация системы в широком смысле.

Location: London, United Kingdom - Deliveroo Our Global Structure Deliveroo is now part of DoorDash, bringing together teams with even greater reach, scale, and ambition. Depending on your role, you may collaborate with teammates, systems, and leaders across DoorDash and Wolt. Together, we’re unlocking new possibilities as one global team. Senior Software Engineer, GenAI Platform About the Team Deliveroo's GenAI Platform team sits within Machine Learning Platform and builds the shared infrastructure that helps DoorDash, Wolt, and Deliveroo teams safely bring GenAI-powered products, agents, automation, and personalisation to production. Our mission is to increase the velocity of business impact from GenAI. A central pillar of that work is running frontier open-weight LLMs and VLMs (such as GLM, Qwen, Kimi, and DeepSeek) ourselves — real-time GPU serving, high-throughput batch inference, and fine-tuning on autoscaling GPUs — delivering large cost and latency wins (for example, a billion embeddings produced roughly 20× cheaper and visual models served roughly 72% cheaper). We also own core platform surfaces including the LLM Gateway, Agent Gateway, evals infrastructure, guardrails, and cost attribution. About the Role You will join a small, high-leverage team building production infrastructure for Generative AI at Deliveroo and DoorDash, leading the design and architecture of our open-weights model platform spanning inference and fine-tuning: real-time GPU serving, high-throughput batch inference, and model fine-tuning. You’ll set technical direction across model serving and inference engines, fine-tuning and training pipelines, GPU autoscaling and utilization, batch pipelines, backend services, and observability, and mentor engineers as you go. This role is ideal for a senior engineer who enjoys owning ambiguous, high-impact systems and pushing the cost/performance frontier of GPU inference and fine-tuning in a fast-moving technical area where product needs, model capabilities, vendor ecosystems, and cost/performance tradeoffs are evolving quickly. You’re excited about this opportunity because you will… Lead the design of infrastructure that helps teams move GenAI ideas from prototype to production, increasing the velocity of business impact from AI across the company. Own and evolve our open-weights serving stack — real-time GPU endpoints, high-throughput batch inference, and fine-tuning (SFT/DPO/LoRA) — alongside the LLM Gateway, Agent Gateway, evals infrastructure, guardrails, and cost attribution. Architect scalable, high-performance systems for model serving, batch inference, GPU autoscaling, and fine-tuning that power real customer and internal automation use cases Push the cost and latency frontier of GPU inference — turning batch jobs that took days into hours and cutting inference cost by multiples — while giving product teams a clean choice across open-weight and closed-source models with reliability, fallback, observability, and cost controls built in. Build platforms that support rapid experimentation while meeting production standards for latency, scale, monitoring, SLOs, playbooks, and operational excellence. Partner closely with — and raise the technical bar for — ML engineers, product engineers, data scientists, and platform teams across DoorDash, Wolt, and Deliveroo to turn emerging GenAI capabilities into durable platform primitives. Set technical direction for the future of the company's centralised GenAI platform — including emerging directions such as reinforcement learning (RLHF/RLVR), agent optimisation, and other post-training and agentic techniques — enabling the next generation of AI-powered products, agents, automation, and personalisation. We’re excited about you because… BSc, MSc or PhD in Computer Science or equivalent 5+ years of industry experience in software engineering Deep backend engineering fundamentals, especially in Python and distributed systems. Track record of designing and owning production services, APIs, data pipelines, or ML infrastructure at scale. Experience operating systems in production, including observability, debugging, reliability, incident response, and performance/cost optimization. Deep hands-on experience with LLM inference and/or fine-tuning of open-weight models in production — serving (latency, throughput, batching, autoscaling, GPU utilization) and/or fine-tuning (SFT/DPO/LoRA). Demonstrated technical leadership: leading design across ambiguous, fast-moving technical areas, mentoring engineers, and turning customer use cases into reusable platform capabilities Proficiency in using AI coding tools (e.g., Claude Code, Codex, Cursor) in the full software development lifecycle, including designing, generating code, testing, monitoring and releasing software Nice To Haves Experience with LLM inference engines and serving frameworks (e.g., vLLM, SGLang, TensorRT-LLM) in production Experience with distributed/multi-node fine-tuning and training pipelines (SFT, DPO/RLHF, LoRA), including data preparation and evaluation GPU performance work — multi-node/distributed inference, KV-cache/memory optimisation, quantisation (FP8/INT8/AWQ/GPTQ), or cold-start/throughput tuning Experience with Kubernetes, cloud infrastructure (AWS/GCP), GPUs, serverless/elastic GPU platforms (e.g., Modal), or high-throughput batch systems Experience with LLM gateways, model routing, vendor abstraction, or cost attribution Experience building developer platforms, internal platforms, or self-serve infrastructure Experience building and deploying AI agents or MCP servers in production Experience with eval systems, LLM observability, tracing, RAG, search, or vector databases Diversity, Equity and Inclusion At Deliveroo, we know that a great workplace reflects the world around us and that true diversity and inclusion make us stronger, more creative, and better at what we do. We’re committed to fostering an environment where everyone can do their best work and feel they belong. We believe in equality of opportunity and welcome candidates from all backgrounds regardless of age, gender, ethnicity, disability, sexual orientation, gender identity, socio-economic background, religion, or belief. If you have a disability or long-term health condition and need support to apply for one of our roles, or require any reasonable adjustments during the recruitment process, you’ll have the opportunity to let us know once you’ve submitted your application. We’ll share details on how to request support so we can ensure you have a fair and equitable experience. If you’re excited about making a real impact in a fast-moving marketplace and growing your career alongside ambitious, supportive teams, we’d love to hear from you! Beware of recruitment scams: DASH Brands will never ask you to pay money or share sensitive financial information during hiring — learn more about DASH Brands' legitimate recruiting process at careersatdoordash.com/recruitment-scam-awareness .

- Опыт работы в качестве Ведущего Разработчика; - Знание механизмов платформы 1С 8.3;

Разрабатывать backend-сервисы на Python 3.10+ / FastAPI в рамках ИИ-платформы. Проектировать и поддерживать микросервисную и event-driven архитектуру.

Основной фокус роли — связка native-платформ iOS/Android с Flutter: работа с native APIs, embedders и platform adaptation code, то есть скорее глубокая работа на стороне iOS/Android и интеграции этих компонентов с Flutter.

• Управлять полным циклом проекта, защищать интересы акционера перед подрядчиками, регуляторами и партнёрами; координировать работу СП и взаимодействие с китайской стороной.

💎 Разработка и поддержка backend-сервисов в микросервисной архитектуре для системы защиты от мошенничества: обработка транзакций в реальном времени, ответственность за производительность, мониторинг и разбор инцидентов, работа с инженерами, продактами и дизайнерами.

— Мы усиливаем научную команду и ищем руководителя группы по флотации — человека, который сочетает научную глубину с инженерной дисциплиной и умеет доводить исследования до внедрения на производстве. Если вы руководите научно-исследовательскими работами, экспериментальной деятельностью, отвечаете за результаты и оформление документации — откликайтесь.