AI-инженер (LLM, агенты, AI product) вакансии - в офисе

vazifala r: ai/ml yechimlarini amaliy jarayonlarga joriy etish, katta hajmdagi ma’lumotlarni qayta ishlashni avtomatlashtirish va ai modellarini backend tizimlariga integratsiya qilish. talabla r: python’ni yaxshi bilish, llm, vision/vlm yoki ml bilan amaliy ishlash tajribasi.

Allen Control Systems is looking for a Director of Computer Vision and Machine Learning to lead perception and tracking strategy, manage teams, and deliver production computer vision and machine learning systems.

Задачи: — Участвовать в художественной разработке проекта: создавать и дорабатывать визуальные референсы, карты персонажей и локаций, помогать формировать единый облик мира сериала. — Работать с отснятым материалом и референсами, выстраивать процесс генерации сцен. — Добиваться убедительного фотореализма: естественных фактур, проработанного света, цвета, глубины резкости, зерна и т.д. — Превращать съёмку актёров в грейруме в сцены кинематографического качества, сохраняя актёрскую игру, действия и расположение персонажей. Требования: — Опыт создания не менее двух минут связного материала на основе актёрской съёмки. — Наличие нескольких планов, складывающихся в цельную сцену или сцены, с сохранением облика персонажей и качества изображения на всём протяжении. — Обязательно владение Premiere Pro или DaVinci Resolve. — Рассматриваются специалисты, работающие в Final Cut Pro X. — Опыт работы с ИИ-агентами, например Codex или Claude, понимание устройства скиллов, умение применять их в производственном процессе, а при необходимости создавать и дорабатывать собственные. Условия: — Работа в штате, очно в офисе в центре Москвы. — Удалённый формат не предусмотрен. — График 5/2, с 10:00 до 19:00. — Уровень заработной платы обсуждается по итогам собеседования с учётом опыта и представленной работы. Как откликнуться: — Написать в Telegram: @igorvandik https://t.me/igorvandik. — В первой строке сообщения указать: «ИИ-дженералист — работа с актёрскими референсами». — Прислать одну работу, содержащую не менее двух минут связного финального материала, показать исходную актёрскую съёмку и созданную на её основе генеративную сцену, желательно оформить сравнение рядом или в формате «окно в окне». Также рассматриваются специалисты по другим направлениям: — 3D-блокинг: прислать работу с финальным материалом не менее двух минут, показать сравнение блокинга и результата, первая строка отклика: «ИИ-дженералист — 3D-блокинг». — Фулл ИИ: создавать полностью генеративные фотореалистичные сцены длительностью не менее трёх минут, первая строка отклика: «ИИ-дженералист — фулл ИИ». — ИИ-анимация: создавать цельные анимационные работы, воспринимающиеся как качественный 3D-мультфильм, важны выразительные персонажи, осмысленное действие и последовательный визуальный стиль, первая строка отклика: «ИИ-дженералист-аниматор». Для всех направлений: — Написать пару слов о себе и своём опыте, указать роль в представленной работе и оставить номер телефона. — Приложить ссылку на один конкретный кейс по выбранному направлению, а не на общее портфолио. — При отклике на несколько направлений отправить отдельное сообщение по каждому с соответствующим заголовком и одной релевантной работой.

Основная задача — создавать на основе съёмки актёров в грейруме полностью генеративные сцены кинематографического качества. Premiere Pro или DaVinci.

Управлять командой DS и MLE: управлять бэклогом, развивать компетенции, проводить найм и растить сотрудников; Формировать стратегию и дорожную карту развития AI-решений для Управления зарплатных проектов;

• Практический опыт работы с AI и создания AI-решений. • Понимание жизненного цикла разработки программных решений.

• Коммерческий опыт разработки на Python от 3 лет. • Понимание эмбеддингов, поиска, реранкинга и борьбы с галлюцинациями.

{COMPANY} is looking for an AI Strategist to deploy its platform, advise financial-services customers, and drive measurable value and commercial growth.

SpAItial is looking for an ML Inference Engineer to build and operate scalable inference services and production APIs.

Location: Vladivostok Workplace: onsite ABOUT US Xsolla is a global commerce company with robust tools and services to help developers solve the inherent challenges of the video game industry. From indie to AAA, companies partner with Xsolla to help them fund, distribute, market, and monetize their games. Grounded in the belief in the future of video games, Xsolla is resolute in the mission to bring opportunities together, and continually make new resources available to creators. Headquartered and incorporated in Los Angeles, California, Xsolla operates as the merchant of record and has helped over 1,500+ game developers to reach more players and grow their businesses around the world. With more paths to profits and ways to win, developers have all the things needed to enjoy the game.   For more information, visit xsolla.com.   ABOUT YOU We are looking for a Junior Developer (AI Engineering) to join the Xsolla Partner Network (XPN) team, a platform that connects game studios and publishers with content creators. The position involves participation in all stages of work on an automated content generation system: from stabilising the existing vertical video pipeline for YouTube Shorts, TikTok and Instagram Reels to developing the agentic architecture, launching the virtual AI creator direction and building analytics on the results. Working with the team means applying cutting-edge generative models and agentic tools to a real production product every day, seeing the direct effect of your work in content metrics, and learning from the inside how the games industry, the creator economy and content-driven game promotion work. The developer will work with a live system that has a real audience and measurable results, make technical decisions independently, build expertise in generative AI and take on the role of the project's technical owner as it grows. Responsibilities Take over the existing vertical video generation pipeline (script → voiceover → visuals → subtitles → editing → auto-posting → tracking links) for YouTube Shorts, TikTok and Instagram Reels, understand it from the docs and code, and bring it to stable operation. Grow the pipeline: improve content quality and virality, scale up generation volume, reduce cost and turnaround time per video. Keep track of new generation models and services (video, image, voice, LLM), test them quickly and replace outdated parts of the pipeline with better or cheaper ones. Launch and develop the virtual AI creator direction: a consistent character, voice, style and content plan. Design and improve agents: write and debug prompts and sub-agent roles, validation between phases, context and cost control. Build a dashboard for running generation and tracking content analytics (views, clicks, conversions, cost per video). Automate publishing: platform integrations, posting from mobile devices, scaling channels. Write clean, testable, documented code, including unit and integration tests. Requirements Solid Python, basic bash. Hands-on experience with LLM APIs (Claude, OpenAI, etc.) and agentic pipelines: a real project, pet project or hackathon entry you can show and talk through. Experience using Claude Code, Cursor or similar tools as a working environment, not just as autocomplete. Understanding of prompt engineering basics: defining an agent's role, limiting context, getting stable structured output. Understanding of Git, team development and Docker basics. Ability to work with REST APIs and webhooks, basic understanding of databases (SQL). Understanding of vertical content: games, creators, streams, TikToks, Shorts, how video discovery works, what shadowbans are, how mobile posting farms are set up. A big plus if this is your hobby: your own channel, editing, streaming, experiments with AI generation. Ownership: the ability to take a task end to end and deliver a result without external supervision. Initiative: your own proposals on improving the pipeline, new models and automation, and the readiness to implement them yourself. Ability to figure out someone else's system from code and documentation, reconstruct the logic and ask the right questions. Independence in technical decisions and openness in communication: flag problems immediately. Product thinking: measuring results by content metrics (views, retention, clicks) and cost per video. Ability to explain the system's capabilities and limitations to non-technical colleagues. Experimental mindset: fast hypothesis testing and the willingness to drop what did not work. Desire to grow with the project: from stabilising the current pipeline to becoming its technical owner. English at the level of reading technical documentation (Intermediate+). Nice to have Experience with video and image generation via API ( fal.ai , Kling, Runway, Seedance, Nano Banana, etc.), TTS and speech recognition (Whisper and similar). ffmpeg: assembly, normalisation, subtitles, overlays. Experience building reliable calls to paid APIs: retries, idempotency, cost tracking. n8n or other workflow tools. Telegram bots, YouTube / TikTok / Instagram / Discord APIs. Android / mobile device automation. Supabase or PostgreSQL. Experience with GCP, Docker, Kubernetes. Observability and monitoring (Datadog, OpenTelemetry). Your own channel, pet projects, open-source experience or hackathon participation. Benefits   Convenient working tools: New Mac laptop and any other necessary equipment for your efficient work. Google Workspace, Atlassian, Jira, Slack   Professional growth: Exchange of experience between employees within the company Opportunity for professional growth within the company and department Obtaining expertise in various areas and products of the company   More perks Possibility of flexibility working Access to the corporate library No dress code Comfortable and new office environment The duties and responsibilities of this position may evolve over time to support the organization’s goals and individual growth. This job description is intended to outline the general nature and level of work being performed and is not intended to be an exhaustive list of all duties, responsibilities, and qualifications required. By submitting your application, you consent to Xsolla conducting background checks, where permitted by law, after the final interview stage. All checks will comply with local regulations, and your information will be handled confidentially. Xsolla takes your privacy seriously and will not sell or externally distribute any personal data received during the hiring process. In accordance with applicable data protection laws, Xsolla is committed to protecting your personal information and respecting your privacy. For any inquiries related to data privacy, please contact: careers@xsolla.com Explore more opportunities at: https://xsolla.com/careers

Основное: Python, FastAPI, Git, Docker. AI фреймворки: OpenAI SDK, LangChain / LangGraph, LlamaIndex.

Задачи: — Разработка и валидация возможностей взаимодействия человека и машины для автономных летательных аппаратов. Требования: — Опыт работы с ROS. — Знание C# и Python. — Опыт работы с Linux. — Навыки работы с системами контроля версий. — Опыт в симуляции, тестировании полётов, техническом письме, байесовских методах, юнит-тестировании, алгоритмах и сенсорном слиянии. Условия: — Работа в офисе в Бостоне. — Зарплата от 165000 до 220000 долларов в год.

написание и ведение сопроводительной документации заведение задач на разработку и тестирование Требования понимание принципов работы с генеративным ИИ и сопутствующими технологиями.

Location: Boston, MA Workplace: OnSite ABOUT US At LangChain, our mission is to make intelligent agents ubiquitous. We build the foundation for agent engineering in the real world, helping developers move from prototypes to production-ready AI agents that teams can rely on. We began as widely adopted open-source tools and have grown to also offer a platform for building, evaluating, deploying, and operating agents at scale. With $125M raised at Series B from IVP, Sequoia, Benchmark, CapitalG, and Sapphire Ventures, we’re at a stage where we’re continuing to develop new products, growth is accelerating, and all team members have meaningful impact on what we build and how we work together. LangChain is a place where your contributions can shape how this technology shows up in the real world. Today, our platform includes LangSmith (Observability, Evaluation, Deployment, Fleet, and Sandboxes), our open source frameworks (LangChain, LangGraph, and Deep Agents), and the newly launched LangSmith Engine for autonomous agent improvement. We have 100M+ monthly open source downloads, 6,000+ active LangSmith customers, and 5 of the Fortune 10 use LangSmith in production (+ 35% of the Fortune 500 overall), including teams at Klarna, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, LinkedIn, Monday.com, Nvidia, and Bridgewater. ABOUT THE TEAM The Deployed Engineering team works directly with companies building and running AI agents in production, helping turn ideas and prototypes into systems teams can rely on. This is a hands-on, highly technical team that partners closely with customer engineers across the full lifecycle, from pre-sales evaluations to post-deployment advisory work. The focus is on achieving the technical win, co-designing agent architectures, and helping customers operate agents reliably at scale using the LangChain suite. Deployed Engineers sit at the intersection of engineering, product, and go-to-market, shaping how LangChain is adopted in the field and feeding real-world insights back into the platform. ABOUT THE ROLE The Deployed Engineer…You’ll work on some of the hardest problems in applied AI — not demos, not research, but systems that real teams depend on in production. The feedback loop is fast, the impact is visible, and the work you do directly shapes how AI agents are built in the real world. WHAT YOU’LL DO - Co-architect and co-build production AI agents with customer engineering teams - Own the technical win in pre-sales by designing POCs, answering deep technical questions, and guiding evaluations - Help customers deploy and operate agent-based applications such as conversational agents, research agents, and multi-step workflows - Advise customers post-sale on architecture, best practices, and roadmap-level decisions - Run technical demos, trainings, and workshops for developer audiences - Surface field feedback and contribute reusable patterns, cookbooks, and example code that scale across customers - Occasionally contribute code upstream when it meaningfully improves customer outcomes WHAT YOU’LL BRING - 3+ years in a relevant technical role (software engineering, customer engineering, solutions engineering, founding/product engineering), ideally in a startup or scale-up - Strong Python, JavaScript and systems fundamentals - Have designed agent-based or LLM-powered applications beyond simple API calls, including multi-step workflows, orchestration, and failure handling - Are comfortable working directly with customers during POCs, architecture reviews, and technical evaluations - Can explain technical tradeoffs clearly and build trust with developer audiences - Take responsibility for outcomes, not just recommendations - Have a bias toward action and enjoy figuring things out as you go - Are excited about operating AI agents in production, not just building demos NICE TO HAVE’S: - You’ve deployed AI agents in production, especially using LangChain, LangGraph, or similar frameworks - Worked with LLM evaluation, observability, or guardrails - Have experience with cloud environments (AWS, GCP, Azure), containers, and basic Kubernetes concepts - Have shipped and operated production software and are comfortable owning systems under real-world constraints COMPENSATION Annual OTE range: $185,000–$300,000 USD Compensation Philosophy: We offer competitive compensation that includes base salary, variable compensation for relevant roles, meaningful equity, benefits, and perks. We are hiring for this role across a range of scope, and the compensation band reflects that. Your offer depends on what we learn about your impact, craft, leadership, and communication during the interview process. We will tell you early where you’re tracking and talk comp with you before offer stage. Team members in the EU, UK, and APAC receive locally competitive benefits aligned with regional norms and regulations. BENEFITS Benefits include medical, dental, and vision coverage, flexible vacation, a 401(k) plan, meals on in-office days in the US and more.

Location: New York, NY Workplace: OnSite ABOUT US At LangChain, our mission is to make intelligent agents ubiquitous. We build the foundation for agent engineering in the real world, helping developers move from prototypes to production-ready AI agents that teams can rely on. We began as widely adopted open-source tools and have grown to also offer a platform for building, evaluating, deploying, and operating agents at scale. With $125M raised at Series B from IVP, Sequoia, Benchmark, CapitalG, and Sapphire Ventures, we’re at a stage where we’re continuing to develop new products, growth is accelerating, and all team members have meaningful impact on what we build and how we work together. LangChain is a place where your contributions can shape how this technology shows up in the real world. Today, our platform includes LangSmith (Observability, Evaluation, Deployment, Fleet, and Sandboxes), our open source frameworks (LangChain, LangGraph, and Deep Agents), and the newly launched LangSmith Engine for autonomous agent improvement. We have 100M+ monthly open source downloads, 6,000+ active LangSmith customers, and 5 of the Fortune 10 use LangSmith in production (+ 35% of the Fortune 500 overall), including teams at Klarna, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, LinkedIn, Monday.com, Nvidia, and Bridgewater. ABOUT THE TEAM The Deployed Engineering team works directly with companies building and running AI agents in production, helping turn ideas and prototypes into systems teams can rely on. This is a hands-on, highly technical team that partners closely with customer engineers across the full lifecycle, from pre-sales evaluations to post-deployment advisory work. The focus is on achieving the technical win, co-designing agent architectures, and helping customers operate agents reliably at scale using the LangChain suite. Deployed Engineers sit at the intersection of engineering, product, and go-to-market, shaping how LangChain is adopted in the field and feeding real-world insights back into the platform. ABOUT THE ROLE The Deployed Engineer…You’ll work on some of the hardest problems in applied AI — not demos, not research, but systems that real teams depend on in production. The feedback loop is fast, the impact is visible, and the work you do directly shapes how AI agents are built in the real world. WHAT YOU’LL DO - Co-architect and co-build production AI agents with customer engineering teams - Own the technical win in pre-sales by designing POCs, answering deep technical questions, and guiding evaluations - Help customers deploy and operate agent-based applications such as conversational agents, research agents, and multi-step workflows - Advise customers post-sale on architecture, best practices, and roadmap-level decisions - Run technical demos, trainings, and workshops for developer audiences - Surface field feedback and contribute reusable patterns, cookbooks, and example code that scale across customers WHAT YOU’LL BRING - 3+ years in a relevant technical role (software engineering, customer engineering, solutions engineering, founding/product engineering), ideally in a startup or scale-up - Strong Python, JavaScript and systems fundamentals - Have designed agent-based or LLM-powered applications beyond simple API calls, including multi-step workflows, orchestration, and failure handling - Are comfortable working directly with customers during POCs, architecture reviews, and technical evaluations - Can explain technical tradeoffs clearly and build trust with developer audiences - Take responsibility for outcomes, not just recommendations - Have a bias toward action and enjoy figuring things out as you go - Are excited about operating AI agents in production, not just building demos NICE TO HAVE’S: - You’ve deployed AI agents in production, especially using LangChain, LangGraph, or similar frameworks - Worked with LLM evaluation, observability, or guardrails - Have experience with cloud environments (AWS, GCP, Azure), containers, and basic Kubernetes concepts - Have shipped and operated production software and are comfortable owning systems under real-world constraints COMPENSATION Annual OTE range: $185,000-$315,000 USD Compensation Philosophy: We offer competitive compensation that includes base salary, variable compensation for relevant roles, meaningful equity, benefits, and perks. We are hiring for this role across a range of scope, and the compensation band reflects that. Your offer depends on what we learn about your impact, craft, leadership, and communication during the interview process. We will tell you early where you’re tracking and talk comp with you before offer stage. Team members in the EU, UK, and APAC receive locally competitive benefits aligned with regional norms and regulations. BENEFITS Benefits include medical, dental, and vision coverage, flexible vacation, a 401(k) plan, meals on in-office days in the US and more.

TypeSafe ищет специалиста по безопасности для работы в офисе в Сан-Франциско. В обязанности входит разработка методов оценки моделей, анализ нежелательного поведения моделей, выявление потенциальных проблем и документирование результатов исследований. Кандидат должен иметь опыт в области безопасности ИИ, интерпретируемости моделей и проведения исследований, связанных с оценкой и выравниванием моделей.

Sierra is looking for a Strategist in Agent Development for Travel Transportation and Hospitality. Задачи: — Разрабатывать и запускать AI агентов — Консультировать корпоративных клиентов Требования: — Опыт работы с AI, системной архитектурой, API, prompt engineering, data model, RAG, conversation design, evaluation Условия: — Работа в офисе в Сан-Франциско — Зарплата от $200,000 до $290,000 в год

Sierra is looking for a Cantonese Speaking Strategist in Agent Development to develop and launch AI agents and advise enterprise customers.