Вакансии

Чем предстоит заниматься — участвовать в обеспечении качества продукта на всех этапах разработки и релизного процесса; — тестировать frontend и backend;

Задачи: — разработка на Python с использованием asyncio, FastAPI — работа с брокерами и таск-менеджерами: Kafka, RabbitMQ, Celery — оптимизация запросов и проектирование схем в PostgreSQL — реализация межсервисных коммуникаций через gRPC, REST, Webhooks — написание фронтенд-компонентов на TypeScript и React — применение DevOps-практик: Docker, CI/CD, развертывание в Kubernetes — использование инструментов мониторинга Prometheus и Grafana — обеспечение покрытия кода тестами Требования: — 3+ года коммерческого опыта на Python с глубоким пониманием asyncio, FastAPI — опыт работы с Kafka, RabbitMQ, Celery — уверенные знания PostgreSQL (query tuning, schema design) — практический опыт межсервисных коммуникаций: gRPC, REST, Webhooks — опыт написания фронтенд-компонентов на TypeScript / React — понимание DevOps-практик: Docker, CI/CD, Kubernetes — опыт работы с инструментами мониторинга Prometheus / Grafana — культура покрытия кода тестами — будет плюсом опыт работы с телеком-инфраструктурой, IP-телефонией, CTI или системами контакт-центров Условия: — full-time — b2b-контракт — почасовая оплата — удалённая работа, строго по России

- Построить надежный процесс подготовки тестовых окружений. - разработать Infrastructure as Code для тестовых окружений;

Location: Canada What The Role Is We're hiring a Staff MLE for the Discovery team to own recommendations and personalization across Babylist's consumer experience — the homepage feed, product recs, search, and the ML-powered systems that make registry building feel effortless. Babylist was built on editorial recommendations: products chosen by people with deep baby gear expertise. That editorial foundation is a big part of why millions of families trust us. We're now building ML-powered personalization on top of it, using one of the richest first-party datasets in parenting. We're early in this work, and we have a real mandate. A small Discovery team has initial retrieval and reranking models live on parts of the site and a steady cadence of A/B tests. We are looking for a Staff MLE who has seen personalization done well at scale and can set the technical direction for where we go next. Registry building is the heart of the Babylist product. Every parent builds a list of dozens of products, from swaddle to stroller, with real stakes (a friend or family member is going to buy these things, and a baby is going to use them). As a universal registry, this registry also typically spans many retailers. This makes registry building on Babylist one of the most interesting personalization problems in consumer e-commerce: latent intent, life-stage progression, multi-stakeholder gift dynamics, deep declarative signal in millions of completed registries, cross-retailer datapoints, and a user who genuinely wants help. If you want to join a mature ML org and tune models at the margins, this isn't the right role. If you've worked inside a strong recommendations team, learned what good looks like, and want to build from zero-to-one at a company earlier in the journey, read on. What You'll Own You'll be the technical lead for personalization on the Discovery team, working alongside a PM, Engineering Manager, Senior MLE and fullstack software engineers. You set where our models go over the next year or two, sequence the bets that get there, and stay deep in building. A few examples of the problems you’ll get to shape: Product recommendations across every surface. Product detail pages, add-next recommendations after someone adds to their registry, vibes/aesthetic representations of products and the shared models underneath them. Personalizing the Feed. Deciding which products, editorial content and social content each family sees, and in what order. Personalizing search. Bringing what we know about a family into search results, and helping decide how much search should share retrieval and ranking with recommendations. Making Addie (our AI registry assistant) better at recs. Working with the team behind Addie so its product suggestions draw on the same models and signals as the rest of the site. In practice, you will: Take a fuzzy business problem from first sketch to production model, and own whether it moved the metric. Make the modeling and architecture calls that span surfaces and are expensive to reverse. Design the evaluation, offline and online, that tells us a model is good before and after it ships. Know when a rule or a simple baseline is the right first step, and when it's time to pursue more advanced approaches. Partner with product, design and data to help shape what's worth building. Coach engineers on the team, and more broadly be an ML expert and resource to the wider engineering org. Who You Are You’ve shipped recommendation and/or personalization systems that reached real users at scale within a consumer product, and can point to the business impact of your work. You’ve done this within a team that did ML well — you know what good looks like, and are motivated to bring that to a company earlier in their ML journey. You bring: Expertise in recommender fundamentals: for example, candidate generation vs. ranking, offline vs. online evaluation, cold start solutions, and handling position and popularity bias. Fluency in the Python ML stack and comfort owning models through deployment and monitoring, including contributions to our application backend. An ability to take an ambiguous problem and start moving before anyone hands you the full picture. A builder's instinct for early-stage ML. You understand effort vs. effectiveness tradeoffs and can advocate for when a rule beats a model, or a model is “good enough” for early learnings. An outcome and user focused mindset. You measure yourself by business impact, and you can connect a model change to user experience & registry adds, conversion or revenue. AI-native daily practice. You're already using AI to move faster and improve your output, and you stay curious about what's coming next. Compensation We post real numbers. For a Canada-based Staff Engineer, the starting base salary range is 299,300 to 372,600 CAD plus a target annual bonus of 20 percent of base. That's total target cash of roughly 359,160 to 447,120 CAD. On top of that you get meaningful equity and a 401(k) match. Where you start in that range depends on your experience, and your pay grows from there with performance and scope. How We Build AI is the default here. Engineers run agentic sessions for most of the work, and a lot of the interesting engineering now lives in the scaffolding that makes the agents good: the eval harnesses, the curated context, custom review skills and fast CI. Agents also triage incidents and handle a big share of support. A human always owns the outcome. The architecture is intentionally simple: one Rails monolith, MySQL and few moving parts. That's deliberate. Simple infrastructure lets us move fast and lets AI reason about the whole system, so the hardest problems are the ones in front of customers. The Stack Core app: Rails, Packwerk, React, TypeScript, Sidekiq Mobile: iOS (Swift), Android (Kotlin) Data, search & events: MySQL, Snowflake, dbt, Airflow, Weaviate, WarpStream, Hex, Sigma Machine learning: deep learning, matrix factorization, retrieval & ranking, AWS SageMaker, MLflow AI & dev tooling: Claude Code, Devin, Linear, CodeRabbit, Warden, LangChain, Bedrock AgentCore, Maxim Infra & ops: AWS, Kubernetes, Terraform, GitHub Actions, Datadog, incident.io, LaunchDarkly Key integrations: Shopify (payments), Iterable (CRM) Why Babylist An engineer, expecting her first baby, couldn't find the registry she wanted. So she built it. That's how Babylist started, and it's still how we work: engineers solving problems for families. Becoming a parent is one of the biggest moments in a person's life. Millions reach it for the first time every year, making thousands of decisions and figuring it out as they go. That's who we build for, and we're a long way from done. Ten million people give gifts through Babylist every year. We did more than $750M in revenue in 2025, up 45 percent over the year before, and we've been profitable for eight years while staying independent. So you can take on a hard, multi-year problem without watching over your shoulder for the next round or the next correction. And the team is small, around 65 engineers, so what you ship stays visible and your scope stays wide. How We Work Remote-first across the US and Canada, and we have been for years. That's not changing. We trust you to own your time and your outcomes, and we get everyone in a room together twice a year. Teams are small, pods of three to five engineers, so nothing you ship disappears into a committee. You'll work shoulder to shoulder with product, design and data, and with the partners across the business who rely on what you ship. You'll also stay close to customers yourself: sitting in on user interviews, watching session recordings, riding along with support. Here that's part of the engineering job, on a regular basis. How We Hire Three rounds, usually two to three weeks start to finish. Recruiter conversation (30 minutes). Trade context: what you want next, what we're building and straight answers on comp, team and remote. Technical screen (1 hour). One round, no AI, language-agnostic. Reach for Google when you normally would. We just want to see how you reason from first principles. Final round (4 hours). Four one-hour interviews: system design, AI-assisted coding with the tools you'd actually use here, product sense and culture and values. You'll hear back either way within 24 to 48 hours. If your timeline is tight, tell us and we'll move faster. Benefits Company-paid medical and fully covered dental and vision A 401(k) match Generous paid parental leave for birthing and non-birthing parents, plus a gradual return-to-work program Winter Wonder Week, a paid company-wide week off at the end of the year A remote-work stipend Mental-health and wellness support A Few Things To Know We record and transcribe interviews to evaluate candidates, in line with applicable privacy laws. We expect you to use AI in your work and we welcome it in the process, but what you submit and say should reflect your own thinking. If you have a family member or close relationship with a Babylist employee, let your recruiter know. Official outreach only ever comes from an @babylist.com address

Location: United States What The Role Is We're hiring a Staff MLE for the Discovery team to own recommendations and personalization across Babylist's consumer experience — the homepage feed, product recs, search, and the ML-powered systems that make registry building feel effortless. Babylist was built on editorial recommendations: products chosen by people with deep baby gear expertise. That editorial foundation is a big part of why millions of families trust us. We're now building ML-powered personalization on top of it, using one of the richest first-party datasets in parenting. We're early in this work, and we have a real mandate. A small Discovery team has initial retrieval and reranking models live on parts of the site and a steady cadence of A/B tests. We are looking for a Staff MLE who has seen personalization done well at scale and can set the technical direction for where we go next. Registry building is the heart of the Babylist product. Every parent builds a list of dozens of products, from swaddle to stroller, with real stakes (a friend or family member is going to buy these things, and a baby is going to use them). As a universal registry, this registry also typically spans many retailers. This makes registry building on Babylist one of the most interesting personalization problems in consumer e-commerce: latent intent, life-stage progression, multi-stakeholder gift dynamics, deep declarative signal in millions of completed registries, cross-retailer datapoints, and a user who genuinely wants help. If you want to join a mature ML org and tune models at the margins, this isn't the right role. If you've worked inside a strong recommendations team, learned what good looks like, and want to build from zero-to-one at a company earlier in the journey, read on. What You'll Own You'll be the technical lead for personalization on the Discovery team, working alongside a PM, Engineering Manager, Senior MLE and fullstack software engineers. You set where our models go over the next year or two, sequence the bets that get there, and stay deep in building. A few examples of the problems you’ll get to shape: Product recommendations across every surface. Product detail pages, add-next recommendations after someone adds to their registry, vibes/aesthetic representations of products and the shared models underneath them. Personalizing the Feed. Deciding which products, editorial content and social content each family sees, and in what order. Personalizing search. Bringing what we know about a family into search results, and helping decide how much search should share retrieval and ranking with recommendations. Making Addie (our AI registry assistant) better at recs. Working with the team behind Addie so its product suggestions draw on the same models and signals as the rest of the site. In practice, you will: Take a fuzzy business problem from first sketch to production model, and own whether it moved the metric. Make the modeling and architecture calls that span surfaces and are expensive to reverse. Design the evaluation, offline and online, that tells us a model is good before and after it ships. Know when a rule or a simple baseline is the right first step, and when it's time to pursue more advanced approaches. Partner with product, design and data to help shape what's worth building. Coach engineers on the team, and more broadly be an ML expert and resource to the wider engineering org. Who You Are You’ve shipped recommendation and/or personalization systems that reached real users at scale within a consumer product, and can point to the business impact of your work. You’ve done this within a team that did ML well — you know what good looks like, and are motivated to bring that to a company earlier in their ML journey. You bring: Expertise in recommender fundamentals: for example, candidate generation vs. ranking, offline vs. online evaluation, cold start solutions, and handling position and popularity bias. Fluency in the Python ML stack and comfort owning models through deployment and monitoring, including contributions to our application backend. An ability to take an ambiguous problem and start moving before anyone hands you the full picture. A builder's instinct for early-stage ML. You understand effort vs. effectiveness tradeoffs and can advocate for when a rule beats a model, or a model is “good enough” for early learnings. An outcome and user focused mindset. You measure yourself by business impact, and you can connect a model change to user experience & registry adds, conversion or revenue. AI-native daily practice. You're already using AI to move faster and improve your output, and you stay curious about what's coming next. Compensation We post real numbers. For a US-based Staff Engineer, the starting base salary range is $233,500 to $290,700, plus a target annual bonus of 20 percent of base. That's total target cash of roughly $280,200 to $348,840. On top of that you get meaningful equity and a 401(k) match. Where you start in that range depends on your experience, and your pay grows from there with performance and scope. How We Build AI is the default here. Engineers run agentic sessions for most of the work, and a lot of the interesting engineering now lives in the scaffolding that makes the agents good: the eval harnesses, the curated context, custom review skills and fast CI. Agents also triage incidents and handle a big share of support. A human always owns the outcome. The architecture is intentionally simple: one Rails monolith, MySQL and few moving parts. That's deliberate. Simple infrastructure lets us move fast and lets AI reason about the whole system, so the hardest problems are the ones in front of customers. The Stack Core app: Rails, Packwerk, React, TypeScript, Sidekiq Mobile: iOS (Swift), Android (Kotlin) Data, search & events: MySQL, Snowflake, dbt, Airflow, Weaviate, WarpStream, Hex, Sigma Machine learning: deep learning, matrix factorization, retrieval & ranking, AWS SageMaker, MLflow AI & dev tooling: Claude Code, Devin, Linear, CodeRabbit, Warden, LangChain, Bedrock AgentCore, Maxim Infra & ops: AWS, Kubernetes, Terraform, GitHub Actions, Datadog, incident.io, LaunchDarkly Key integrations: Shopify (payments), Iterable (CRM) Why Babylist An engineer, expecting her first baby, couldn't find the registry she wanted. So she built it. That's how Babylist started, and it's still how we work: engineers solving problems for families. Becoming a parent is one of the biggest moments in a person's life. Millions reach it for the first time every year, making thousands of decisions and figuring it out as they go. That's who we build for, and we're a long way from done. Ten million people give gifts through Babylist every year. We did more than $750M in revenue in 2025, up 45 percent over the year before, and we've been profitable for eight years while staying independent. So you can take on a hard, multi-year problem without watching over your shoulder for the next round or the next correction. And the team is small, around 65 engineers, so what you ship stays visible and your scope stays wide. How We Work Remote-first across the US and Canada, and we have been for years. That's not changing. We trust you to own your time and your outcomes, and we get everyone in a room together twice a year. Teams are small, pods of three to five engineers, so nothing you ship disappears into a committee. You'll work shoulder to shoulder with product, design and data, and with the partners across the business who rely on what you ship. You'll also stay close to customers yourself: sitting in on user interviews, watching session recordings, riding along with support. Here that's part of the engineering job, on a regular basis. How We Hire Three rounds, usually two to three weeks start to finish. Recruiter conversation (30 minutes). Trade context: what you want next, what we're building and straight answers on comp, team and remote. Technical screen (1 hour). One round, no AI, language-agnostic. Reach for Google when you normally would. We just want to see how you reason from first principles. Final round (4 hours). Four one-hour interviews: system design, AI-assisted coding with the tools you'd actually use here, product sense and culture and values. You'll hear back either way within 24 to 48 hours. If your timeline is tight, tell us and we'll move faster. Benefits Company-paid medical and fully covered dental and vision A 401(k) match Generous paid parental leave for birthing and non-birthing parents, plus a gradual return-to-work program Winter Wonder Week, a paid company-wide week off at the end of the year A remote-work stipend Mental-health and wellness support A Few Things To Know We record and transcribe interviews to evaluate candidates, in line with applicable privacy laws. We expect you to use AI in your work and we welcome it in the process, but what you submit and say should reflect your own thinking. If you have a family member or close relationship with a Babylist employee, let your recruiter know. Official outreach only ever comes from an @babylist.com address

Location: Remote; Japan (Remote); Slovakia (Remote); Spain (Remote); Czechia (Remote); Switzerland (Remote); Estonia (Remote); Singapore (Remote); Mexico (Remote); USA (Remote); Israel (Remote); Kyrgyzstan (Remote); Bulgaria (Remote); Austria (Remote); India (Remote); Iceland (Remote); Hungary (Remote); Ireland (Remote); Canada (Remote); South Africa (Remote); Brazil (Remote); Finland (Remote); UK (Remote); Romania (Remote); Cyprus (Remote); Malta (Remote); Latvia (Remote); Argentina (Remote); Poland (Remote); Uruguay (Remote) Workplace: Remote WHO WE ARE Hi, we're DuckDuckGo, the online protection company and remote-first team of 400+ on a mission to raise the standard of trust online. Founded in 2008 and profitable since 2014, our annual revenue is in the hundreds of millions USD and millions use our browser on Mac https://spreadprivacy.com/introducing-duckduckgo-for-mac/, Windows https://spreadprivacy.com/windows-browser-open-beta/, iOS https://spreadprivacy.com/duckduckgo-privacy-browser-ios14/, and Android https://spreadprivacy.com/introducing-app-tracking-protection/, our search engine https://duckduckgo.com/, and the DuckDuckGo subscription https://duckduckgo.com/pro. We also offer private, useful, and optional AI, including Duck.ai https://duck.ai/, which lets you chat privately with ChatGPT, Claude, and other AIs, all in one place.   Our culture https://duckduckgo.com/how-we-work of trust, inclusivity, and empowered project management underpins everything we do, where each team member takes full ownership of their projects, from scoping and execution to postmortem. If you're seeking end-to-end ownership of your work, you've come to the right place! YOUR TEAM AND ROLE Working on the Privacy Functional Team, you'll lead complex projects across key company-wide objectives. You'll protect the privacy of our users using Go, Node.js, Python, and (sometimes) Perl to write pragmatic, performant, testable code, and work on related projects. Potential projects include: - Designing systems for improving our search indexes and AI features without compromising user privacy - Building new features on top of our Tracker Radar https://spreadprivacy.com/duckduckgo-tracker-radar/ crawler to detect emerging privacy threats - Implementing new privacy features and maintaining existing ones, such as our Web Tracking https://duckduckgo.com/duckduckgo-help-pages/privacy/web-tracking-protections and Email https://duckduckgo.com/duckduckgo-help-pages/email-protection/privacy Protections   As a Senior Privacy Engineer, you'll own major components of our privacy technology, taking responsibility for their maintenance and improvement over time. A key part of the role will also be participation in our privacy review and audit processes, ensuring that all product developments align with our privacy principles. In time, you'll formally mentor other engineers, supporting their career growth and the growth of the Privacy Team at DuckDuckGo. ABOUT YOU - Ability to lead and collaborate on high-impact and complex projects from proposal through post-mortem. - Skills to wrangle vague problems, propose innovative solutions, and execute them effectively, involving others when necessary. - Experience working on privacy solutions in an engineering capacity (e.g. tracker blocker, differential privacy, or similar technology). - Deep technical understanding of online privacy threats and the existing solution space to protect against them. - Proficiency in any of the following: Go, Node.js, Python, Perl. - Excellent communication skills. You can validate your decisions and communicate them clearly to a variety of audiences, including non-technical stakeholders. COMPENSATION $178,500 USD annually and stock options. Compensation is transparent across the organization, and all team members within the same professional level and global region receive the same compensation.   Eligibility for company-sponsored health benefits is limited to team members based in the United States. This program does not extend to team members located in other countries, such as Canada or the UK.   Our Team Member Support Guide https://duckduckgo.com/assets/hiring/team_support_guide.pdf explains how we prioritize your wellbeing including paid parental leave, office setup, and co-working allowances. HIRING PROCESS Hiring works best when it's a two-way street. Learn how we help you get to know DuckDuckGo, envision your future role here, and find out more about how we hire https://duckduckgo.com/how-we-hire. DIVERSITY, EQUITY AND INCLUSION DuckDuckGo provides equal work opportunities to all team members and applicants, and it prohibits discrimination and harassment of any type on the basis of race, color, ethnicity, caste, religion, age, sex (including pregnancy), national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by our policies or federal, state, or local laws. We want to ensure that our hiring process is accessible. If you need reasonable accommodation for any part of the application process because of a medical condition or disability, please send an email to careers@duckduckgo.com to let us know the nature of your request. PLEASE NOTE THAT: - You’ll be required to attend meetings on camera via video conferencing - Expect to travel at least two times a year: once for our all-hands meetup and again for a team retreat (each around 4-5 days). While extenuating circumstances may impact attendance, everyone is strongly encouraged to attend. - While we offer a flexible work arrangement with no core hours, expect an average full-time commitment of 40 hours per week. - A successful candidate must pass a background check as a condition of joining the team. - By applying for this role, you confirm that all information submitted is accurate and complete. You further acknowledge that providing false or fraudulent information during the application process is cause for denial of an offer, revocation of any existing offer, or other adverse action, up to and including termination after the start of your commencement of work. Disclosure Statement: Use of AI in Hiring Process As part of our commitment to enhancing our recruitment process, we utilize artificial intelligence (AI) technology to assist in reviewing and summarizing job applications and test projects, including those tools integrated into our recruitment vendor platforms. We use AI to flag potentially fraudulent applications, analyze and summarize applicants’ experience, interviews, and project performance, and help streamline our selection process. Key Principles: - Data Privacy: All information provided in your application will be handled in accordance with our Recruiting Privacy Policy https://duckduckgo.com/static-assets/files/pages/careers/DuckDuckGo-Recruiting-Privacy-Policy-effective-September-30-2025.pdf. We ensure that your personal information is protected and used solely for recruitment purposes. - Human Oversight and Accountability: The AI technology is designed to support our hiring team by providing insights and summaries of applications and evaluations of test projects against scoring rubrics. All final evaluations and hiring decisions, however, will be made by our hiring team, who will consider the AI's input alongside other factors. - Transparency: We believe in transparency regarding our hiring practices. If you have any questions about how AI is used in our recruitment process, please feel free to reach out to us. By submitting your application, you acknowledge and consent to the use of AI technology in our review process. If you would like to request an alternative selection process, please contact us as at careers@duckduckgo.com. Thank you for your interest in joining DuckDuckGo! #LI-DNI

☁️ Strong AWS experience: 3+ years designing cloud-native architectures on it. 🔧 6+ years architecting and running CI/CD (Jenkins, GitLab, GitHub Actions, Argo CD).

- Задачи: разработка платёжных микросервисов (ledger, KYC, anti-fraud, лимиты), проектирование схем данных и асинхронного обмена, API-контракты, unit/integration/e2e-тесты, релиз и разбор инцидентов. - Требования: глубокое понимание event loop, конкурентности, производительности;

• разрабатывать и оптимизировать 3D-визуализацию на WebGPU; • работать с LuciadRIA и большими наборами 3D-данных;

Looking for an engineer who has hands-on experience building AI agents that actually execute workflows, not just chat interfaces. You’ll work on agents that connect to APIs and internal systems, use tools, retrieve context, make decisions across multiple steps, and recover when something fails.

Location: (EU) We are the creators of a new fintech era! Our mission is to revolutionize the world by making blockchain technology accessible to everyone in everyday life. WhiteBIT is a global team of more than 1,500 professionals united by a shared vision of shaping the future. We are building our own blockchain ecosystem to ensure maximum transparency and security for over 8 million users worldwide. Our cutting-edge solutions, rapid adaptation to market challenges, and technological excellence set us apart from traditional companies. Our official partners include Juventus, FC Barcelona, Lifecell, FACEIT, and VISA. Join us as an Network Engineer / IT System Administrator. Requirements : Experience & Technical Skills: Experience: 3+ years in a Network Engineer or IT Systems Administrator role. Networking Equipment (Must-have): Hands-on experience configuring and administering Juniper, Fortinet, and Cisco hardware (routers, switches, firewalls, and wireless APs). Network Security & Protocols: Strong proficiency in Fortinet NGFW, VPNs (IPsec/SSL), BGP/OSPF, VLAN segmentation, and traffic engineering. AAA & 802.1X: Practical experience deploying and managing RADIUS (FreeRADIUS, Cisco ISE, or FortiAuthenticator) and 802.1X network access control. SaaS & Automation: IT Services & SaaS: Proven background managing corporate SaaS environments and identity lifecycle (Google Workspace, Atlassian Suite, Slack, SSO/IdP). Scripting & Automation: Hands-on scripting skills (Bash, Python, or PowerShell) and experience using APIs / Webhooks for workflow automation (Jira, Slack, CRM integrations). Language Requirements: Ukrainian and/or Russian: Native or Fluent (C1–C2). Mandatory requirement driven by the need for instant, barrier-free communication during high-severity incidents to minimize MTTR and strictly maintain core SLAs. English: Intermediate (B1–B2) for technical documentation, vendor communication, and raising support cases (e.g., TAC). Nice-to-Have: Vendor certifications such as CCNA/CCNP, JNCIA, or Fortinet NSE 4/5. Responsibilities : Network Equipment & Perimeter: Configure, manage, and harden multi-vendor network infrastructure across Juniper, Fortinet, and Cisco (routers, switches, APs, and Fortinet NGFWs). Access Control & AAA: Administer network authentication systems using RADIUS (FreeRADIUS, Cisco ISE, FortiAuthenticator) and 802.1X for corporate wired and wireless networks. SaaS & IT Services: Manage user identities, access permissions, and system health across core corporate platforms (Google Workspace, Atlassian Suite, Slack, SSO/IdP). Workflow Automation: Build and maintain automation scripts (Bash, Python, or PowerShell) and API integrations connecting internal services (Jira, Slack, CRM). Incident Management & SLAs: Act as Tier 3 escalation support for network and SaaS service outages, ensuring fast MTTR and high availability SLAs. Routing, VPNs & Monitoring: Maintain site-to-site IPsec/SSL VPNs, core routing (BGP/OSPF), and observability tools (Zabbix, Syslog, SNMP) to catch anomalies proactively. Working terms Immerse yourself in Crypto & Web3: Master cutting-edge technologies and become an expert in the most innovative industry. Work with the Fintech of the Future: Develop your skills in digital finance and shape the global market. Take Your Professionalism to the Next Level: Gain unique experience and be part of global transformations. Drive Innovations: Influence the industry and contribute to groundbreaking solutions. Join a Strong Team: Collaborate with top experts worldwide and grow alongside the best. Work-Life Balance & Well-being: Modern equipment. Comfortable working conditions, and an inspiring environment to help you thrive. 22 business days of paid leave. Additional days off for national holidays. Health insurance.

We are looking for an experienced Data Engineer to join a project focused on data engineering, pipeline development, and migration within a cloud-based environment. - Solid professional background in Data Engineering.

Важен опыт работы с платёжными системами.

Проект: Специалист привлекается для поддержки AI-команды (RnD AI Team), которая реализует портфель ИИ-инициатив для бизнес-подразделений компании на внутренней платформе Dify (LLM-агенты, RAG, классификация, извлечение данных из документов, интеграции через API/MCP). Подготовка тест-кейсов на основании требований и критериев приемки инициатив;

💎 Опыт управления рекламными бюджетами от $500к в месяц, AI-native, Strong English.

Looking for an engineer who has hands-on experience building AI agents that actually execute workflows, not just chat interfaces. You’ll work on agents that connect to APIs and internal systems, use tools, retrieve context, make decisions across multiple steps, and recover when something fails.

• Investigate and resolve technical and product-related issues. • Analyse logs, application behaviour and connectivity.