Вакансии

Responsibilities: — Build agentic tools — Develop evaluation systems — Create engineering workflow infrastructure The role involves working with infrastructure, data pipelines, LLMs, failure analysis, multi-agent orchestration, RAG, tool calling, evaluation, MCP, software engineering, DevOps, chip development, fine-tuning, agents, GPU kernels, and context management. Location: San Jose, CA, onsite.

Создавать и оптимизировать подходы к оценке качества моделей и AI-пайплайнов (RAG, LLM, OCR). Построение online/offline метрик качества, написание методологий оценки для мониторинга качества и регрессионного тестирования.

Location: San Francisco Perplexity is AI for people who expect more. This role brings that same standard to how our data team works, with AI at the center of everything we do. We're looking for someone who's been a great data scientist, analytics engineer, or data engineer: the kind of person who knows which metric actually matters, can design an A/B test that answers the real question, has gone deep on a data model because something didn't add up, and has decided that the highest-leverage thing they can do next is build AI systems that fundamentally change how data science gets done. You'll build AI agents and internal systems that can increasingly handle end-to-end analysis workflows: forming hypotheses, writing and running queries, interpreting results, and drafting recommendations with the right evaluation, review, and guardrails. You'll build the retrieval infrastructure and evaluation loops that let AI systems query the warehouse reliably. You'll create workflows that detect, diagnose, and help fix data issues before they become company-wide problems. You'll build the infrastructure that multiplies what a small data team can ship. You'll join a data team that's already using AI across its work. The next step is turning those individual workflows into scalable systems, shared tools, and an AI-native operating model that becomes a benchmark for how modern data teams work.   WHAT YOU'LL DO - Build AI agents that do data science - not just SQL copilots, but systems that can safely explore data, form hypotheses, run queries, interpret results, and generate actionable recommendations with clear evaluation and human review loops. - Make AI systems query the warehouse reliably - build the retrieval infrastructure and evaluation loops that let agents use our semantic context and metadata accurately. - Accelerate the AI-native data workflow - turn the best existing AI-assisted workflows into repeatable systems, reusable tools, and patterns the whole data team can adopt. - Automate the data lifecycle - build self-healing pipelines, automated dbt model generation and validation, data quality agents, and diagnosis workflows that reduce manual firefighting. - Ship AI-powered experiment analysis - build agents that interpret A/B test results, flag statistical issues, identify likely drivers, and draft ship/no-ship recommendations. - Turn the data team into a product team - build internal data products that stakeholders use every day, replacing ad hoc requests with self-serve AI interfaces. - Own the full lifecycle - identify high-leverage problems, prototype with LLMs, evaluate accuracy, design the UX, ship to production, and monitor quality over time. WHAT WE'RE LOOKING FOR - 6+ years in data science, analytics engineering, data engineering, or a related role. You've been close enough to real data work to know what should and should not be automated. - Deep SQL and analytics judgment - you can reason through metrics, experiments, data models, and messy warehouse reality without relying on a tool to think for you. - Strong product sense - you understand what stakeholders actually need, what makes a workflow adoptable, and how to turn a prototype into a product people use. - Hands-on LLM experience - you've built with frontier models, agents, RAG systems, evals, or AI-powered workflows and have opinions about where they work and where they fail. - Pipeline and modeling fluency - you've worked with dbt, warehouse schemas, data quality issues, and the practical tradeoffs behind durable data systems. - Builder mentality - you see a manual process and immediately think about how to systematize it. You ship fast, measure quality, and iterate. - Autonomy - this is a new function. You'll help define the roadmap as much as execute it. BONUS - Experience building production AI agents or agent evaluation systems. - Experience with Snowflake, semantic layers, or metadata systems. - Experience building internal tools, Slack bots, CLIs, or developer productivity products that people actually used. - Strong experimentation background, including metric design and statistical interpretation. - Experience with BI tools and the judgment to know what should be automated versus kept human-reviewed. - Early-stage startup experience. WHY THIS ROLE - Set the standard for the industry - Perplexity's data team is already using AI across its work. You'll turn that into something other data teams look to as the benchmark. - Build AI with AI - Perplexity builds AI for people who expect more. You'll bring that same level of ambition to how the company works with data. - Frontier models, day one - you're at an AI company with access to frontier infrastructure and people who deeply understand what's possible. - Massive leverage - the systems you build will multiply the output of every data team member and every stakeholder who needs data. - Direct impact - small team, no layers of approval. Idea to shipped system in days, not quarters.

Location: San Francisco Perplexity is AI for people who expect more. This role brings that same standard to how our data team works, with AI at the center of everything we do. We're looking for someone who's been a great data scientist, analytics engineer, or data engineer: the kind of person who knows which metric actually matters, can design an A/B test that answers the real question, has gone deep on a data model because something didn't add up, and has decided that the highest-leverage thing they can do next is build AI systems that fundamentally change how data science gets done. You'll build AI agents and internal systems that can increasingly handle end-to-end analysis workflows: forming hypotheses, writing and running queries, interpreting results, and drafting recommendations with the right evaluation, review, and guardrails. You'll build the retrieval infrastructure and evaluation loops that let AI systems query the warehouse reliably. You'll create workflows that detect, diagnose, and help fix data issues before they become company-wide problems. You'll build the infrastructure that multiplies what a small data team can ship. You'll join a data team that's already using AI across its work. The next step is turning those individual workflows into scalable systems, shared tools, and an AI-native operating model that becomes a benchmark for how modern data teams work.   WHAT YOU'LL DO - Build AI agents that do data science - not just SQL copilots, but systems that can safely explore data, form hypotheses, run queries, interpret results, and generate actionable recommendations with clear evaluation and human review loops. - Make AI systems query the warehouse reliably - build the retrieval infrastructure and evaluation loops that let agents use our semantic context and metadata accurately. - Accelerate the AI-native data workflow - turn the best existing AI-assisted workflows into repeatable systems, reusable tools, and patterns the whole data team can adopt. - Automate the data lifecycle - build self-healing pipelines, automated dbt model generation and validation, data quality agents, and diagnosis workflows that reduce manual firefighting. - Ship AI-powered experiment analysis - build agents that interpret A/B test results, flag statistical issues, identify likely drivers, and draft ship/no-ship recommendations. - Turn the data team into a product team - build internal data products that stakeholders use every day, replacing ad hoc requests with self-serve AI interfaces. - Own the full lifecycle - identify high-leverage problems, prototype with LLMs, evaluate accuracy, design the UX, ship to production, and monitor quality over time. WHAT WE'RE LOOKING FOR - 6+ years in data science, analytics engineering, data engineering, or a related role. You've been close enough to real data work to know what should and should not be automated. - Deep SQL and analytics judgment - you can reason through metrics, experiments, data models, and messy warehouse reality without relying on a tool to think for you. - Strong product sense - you understand what stakeholders actually need, what makes a workflow adoptable, and how to turn a prototype into a product people use. - Hands-on LLM experience - you've built with frontier models, agents, RAG systems, evals, or AI-powered workflows and have opinions about where they work and where they fail. - Pipeline and modeling fluency - you've worked with dbt, warehouse schemas, data quality issues, and the practical tradeoffs behind durable data systems. - Builder mentality - you see a manual process and immediately think about how to systematize it. You ship fast, measure quality, and iterate. - Autonomy - this is a new function. You'll help define the roadmap as much as execute it. BONUS - Experience building production AI agents or agent evaluation systems. - Experience with Snowflake, semantic layers, or metadata systems. - Experience building internal tools, Slack bots, CLIs, or developer productivity products that people actually used. - Strong experimentation background, including metric design and statistical interpretation. - Experience with BI tools and the judgment to know what should be automated versus kept human-reviewed. - Early-stage startup experience. WHY THIS ROLE - Set the standard for the industry - Perplexity's data team is already using AI across its work. You'll turn that into something other data teams look to as the benchmark. - Build AI with AI - Perplexity builds AI for people who expect more. You'll bring that same level of ambition to how the company works with data. - Frontier models, day one - you're at an AI company with access to frontier infrastructure and people who deeply understand what's possible. - Massive leverage - the systems you build will multiply the output of every data team member and every stakeholder who needs data. - Direct impact - small team, no layers of approval. Idea to shipped system in days, not quarters.

Location: Belgrade, Serbia; Cluj-Napoca, Romania; Dnipro, Ukraine; Kharkiv, Ukraine; Krakow, Poland; Kyiv, Ukraine; Larnaca, Cyprus; Lodz, Poland; Lublin, Poland; Lviv, Ukraine; Odesa, Ukraine; Remote.BG, Bulgaria; Remote.GE, Georgia; Remote.PL, Poland; Riga, Latvia; Sofia, Bulgaria; Tbilisi, Georgia; Varna, Bulgaria; Warsaw, Poland; Wroclaw, Poland; Yerevan, Armenia We are looking for an AI Governance & Risk Analyst to support AI policy implementation, risk assessments, and governance activities across AI use cases. You will help define guardrails, support reviews, and improve oversight for AI systems and related vendors. This project supports the safe and practical adoption of AI capabilities within a logistics focused platform. You will help close governance gaps, guide risk tiering, and support controls for AI solutions throughout their lifecycle. Turn AI policies into practical local standards Fill policy gaps for agents, non human identities, and Superblocks with lightweight guardrails Run AI use case intake and risk tiering Guide DPIAs and vendor reviews Set agent boundaries for read and write scopes and human in the loop controls Define RAG data access tiers Check audit logging and the impact of vendor model updates Support AI solution lifecycle and decommissioning activities Experience with 2 to 4 years in IT risk, data governance, or information security compliance Experience working in a matrixed organization Understanding of GenAI basics Ability to work with stakeholders across multiple teams Experience with UK or EU DPIAs Experience with GCP governance Experience in logistics environments

Location: London, UK DataArt’s Travel, Transportation & Hospitality Practice is looking for a client-facing professional. The role connects advanced AI tools to real business impact and involves working with client account teams and the AI + Data Lab across 50+ active travel accounts to find high-value use cases, prototype fast, validate with stakeholders, and drive adoption from C-suite to delivery teams. The role covers the journey from prototype to production. You will define how solution quality is measured once a solution is live and design for the controls enterprise clients operate under. Lead ideation and scoping of AI and ML proofs of concept with client account leaders Prototype AI features such as copilots, NLP services, and recommender systems Own the full lifecycle from internal demo and stakeholder validation to client presentation and the path to production Direct internal teams to execute with clarity, velocity, and alignment Build GenAI applications using RAG variations, agentic and hybrid models, cloud AI services, LLM APIs, vector search, and agentic SDKs Integrate AI solutions with Snowflake and Databricks Define how solution quality is measured in production through evaluation sets, accuracy checks, groundedness checks, drift detection, human in the loop checkpoints, and audit records Define reusable AI building blocks and travel specific agent scaffolds for internal delivery practices Shape internal best practice for GenAI across delivery Embed AI into client products and operations for personalization, crew operations, service automation, disruption handling, post booking servicing, reconciliation, and refunds Run workshops with client teams and business leaders Act as a translator and evangelist for responsible AI adoption and ROI Hands on experience with AI services from at least one major cloud such as Azure, AWS, or GCP Experience designing and implementing RAG based applications Experience taking AI solutions beyond pilot into production Experience validating AI solution performance before go live and monitoring it after release Practical understanding of AI evaluation including defining workflow correctness, building test sets, and measuring accuracy and groundedness Strong background with Snowflake and or Databricks for data engineering and AI pipelines Proven success leading client facing proofs of concept or MVPs from inception to delivery at Director level or above Ability to turn ambiguous business goals into scoped, testable, and scalable initiatives Fluency in open source versus commercial model tradeoffs and cost efficient architecture Prior exposure to the travel, transportation, or hospitality sector Experience with vector databases Experience with context engineering Experience with orchestration frameworks such as LangChain or Semantic Kernel Familiarity with fine tuning and understanding of when it is worth the effort Experience with agentic loop engineering Experience with AI observability or evaluation tooling Awareness of responsible AI practices including fairness, explainability, and usage boundaries

Location: Москва Центр практического ИИ — подразделение Сбера, которое занимается созданием AI-решений и агентных систем, а также их внедрением в бизнес-процессы банка. Сейчас мы ищем в команду Senior python-разработчика на проекты по внедрению AI-агентов в hr процессы Сбера (подбор, оценка и развитие компетенций сотрудников и т.д.). Первый этап отбора на эту вакансию – общение с AI-рекрутером. После отклика ждите сообщение от него в телеграмме, диалог займёт примерно 10 минут. Задача AI-рекрутера — уточнить недостающие детали и ускорить рассмотрение вашей кандидатуры. AI-рекрутер только начинает свой путь, поэтому просим относиться с пониманием. Ваш опыт и участие помогут сделать его удобным и полезным для всех! Обязанности ### Вы будете: * анализировать прототипы AI-агентов от Data Scientist’ов * оптимизировать инференс моделей, LLM и RAG-пайплайнов * доводить решения до production-ready кода (тесты, типизация, логирование) * деплоить, настраивать CI/CD и мониторинг * интегрировать агентов с корпоративными API и базами данных (PostgreSQL, OpenSearch), Kafka. Требования ### Мы ожидаем, что вы имеете: * опыт работы разработки на Python от 3 лет, умение писать чистый, тестируемый код * практический опыт доведения прототипов моделей и AI-агентов до продакшена, знакомы с агентными фреймворками и архитектурами (LangGraph, LlamaIndex и др) * опыт разработки RAG-систем, работы с реляционными и векторными базами данных (понимание какую БД когда выбирать) * опыт оптимизации инференса LLM/NLP-моделей * опыт работы с параллелизмом и асинхронностью * готовность иметь дело с широким набором инструментов и технологий: Python, Elastic/OpenSearch, Kafka, FastAPI, Docker, OpenShift, SQL, GPU, BitBucket/GitLab). Условия ### Мы предлагаем: * комфортный современный офис г.Москва, рядом с метро Кутузовская * офисный формат работы (возможно обсудить гибрид после исп.срока с посещением офиса не менее 2х дней в неделю) * ежегодный пересмотр зарплаты и годовая премия * корпоративный спортзал и зоны отдыха * более 400 образовательных программ СберУниверситета для профессионального и карьерного развития * расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа * гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ * подписка Прайм с возможностью совместного использования на трёх близких * вознаграждение за рекомендацию друзей в команду Сбера.

Location: Hybrid About Us At Cloudflare, we are on a mission to help build a better Internet. Today the company runs one of the world’s largest networks that powers millions of websites and other Internet properties for customers ranging from individual bloggers to SMBs to Fortune 500 companies. Cloudflare protects and accelerates any Internet application online without adding hardware, installing software, or changing a line of code. Internet properties powered by Cloudflare all have web traffic routed through its intelligent global network, which gets smarter with every request. As a result, they see significant improvement in performance and a decrease in spam and other attacks. Cloudflare was named to Entrepreneur Magazine’s Top Company Cultures list and ranked among the World’s Most Innovative Companies by Fast Company. At Cloudflare, we’re not looking for people who wait for a polished roadmap; we’re looking for the builders who see the cracks in the Internet that everyone else has simply learned to live with. We value candidates who have the instinct to spot a "normalized" problem and the AI-native curiosity to create a solution using the latest tools. Our culture is built on iteration, leveraging AI to ship faster today to make it better tomorrow, while ensuring that every improvement, no matter how small, is shared across the team to lift everyone up. If you’re the type of person who values curiosity over bureaucracy, and that AI is a partner in solving tough problems to keep the Internet moving forward, you’ll fit right in. Available Location: Northeast What you will do as a Customer Engineer The Customer Engineer (CE) is the core driver of our technical go-to-market strategy: a trusted technical advisor and primary architect of the customer journey. You will bridge the gap between technical strategy and business value—guiding enterprise clients from initial pre-sales technical validation through onboarding, deployment architecture, and full lifecycle expansion. Sitting within the GTM organization and reporting to a Customer Engineering Leader, you will not hand off your customers once an initial agreement is reached. Instead, you will operate as a quota-carrying technologist, leveraging AI-augmented workflows to eliminate administrative toil so you can focus on high-value architectural design and driving measurable business outcomes across the entire customer journey. Key Responsibilities Technical Validation: Partner closely with Account Executives to lead technical deep-dive discovery, capture business requirements, and map complex customer infrastructure - DNS, CDN, Zero Trust, Network Security, Workers - to fit-for-purpose Cloudflare architectures and bespoke solutions. . Define, scope, success criteria, and technical evaluations plans for Proof of Concepts (PoCs) and Technical Validations. Deliver tailored technical presentations, architectural diagrams, and demonstrations that show how Cloudflare solves specific performance, security, and reliability challenges. Proactively address complex technical objections, security compliance questions, and edge-case architectural requirements during the sales cycle. Take accountability for a suitable solution deployment strategy (Customer, Partner or Cloudflare Professional services) being an integral part of all proposals. Customer Adoption & Value Realization: Provide oversight to technical onboarding and implementation, but not execute the implementation, serving as the primary executive and engineering contact for the customer. Design structured migration and onboarding blueprints tailored to the customer’s environment (e.g. DNS migrations, Zero Trust rollouts, WAF tuning guidance), and act as the lead consultant for customer engineering teams, walking them step-by-step through configuration best practices.  Partner with Account Executives to own discovery, demos, onboarding, technical health, risk, and expansion strategy. Proactively monitor customer health scores and telemetry to ensure rapid time-to-value, resolve integration bottlenecks, and reduce deployment risk. Commercial Accountability & Expansion: Carry a variable quota tied directly to new business acquisition and/or ongoing account growth. Be commercially accountable for ensuring deep platform adoption, retaining revenue, and identifying strategic cross-sell and up-sell opportunities based on product usage data. Lead technical Quarterly Business Reviews (QBRs) to translate platform capabilities into return on investment for C-level executives. Trusted Advisory: Act as a trusted technical advisor throughout the entire customer lifecycle, maintaining a continuous, deep relationship with key stakeholders from initial onboarding through maturity, renewal, and expansion. Provide thought partnership on architecture, AI control layers, security posture, and deployment confidence. Identify potential deployment risks or integration bottlenecks early, and provide remediation strategies to de-risk customer outcomes. AI Orchestration & Workflow Automation: Leverage AI agents, Large Language Models (LLMs), and internal platforms to automate routine tasks such as RFP/Security Questionnaire responses, sandbox provisioning, basic telemetry monitoring, and internal/external documentation, reclaiming your time for strategic customer advisory. Education & Enablement: Partner closely with the Enablement Team to design, build, and govern technical resources. Define, document, and educate both internal teams and customers on deliverables such as Standard Operating Procedures (SOPs), architectural best practices, and technical guides to continuously raise the technical baseline across the organization. Minimum Qualifications Experience: Experience in a customer-facing technical role, such as Solutions Engineering, Customer Engineering, Professional Services,  Sales Engineering, Technical Account Management, or as a Forward Deployed Engineer (years of experience required will vary based on Associate, Senior, Staff, or Principal leveling). Architectural Mindset: Ability to white-board, design, and articulate complex enterprise network and security architectures clearly. Consultative & Advisory Skills: Excellent communication skills with the ability to translate deeply technical concepts into business outcomes for both hands-on engineers and C-level executives. Commercial Mindset: Proven track record of operating in a quota-carrying or revenue-aligned environment, with a strong understanding of SaaS metrics, business value translation, and enterprise sales cycles. Communication: Exceptional presentation and stakeholder management skills, capable of bridging the gap between deep technical implementation details and high-level C-suite business strategy. Technical Breadth: We do not expect every candidate to be an expert in every domain. Qualified candidates will demonstrate depth in at least four of the technical areas below: Internet fundamentals: Deep understanding of how the Internet works, including OSI Model layers 1–7, BGP, GRE, TCP/UDP, DNS (authoritative and recursive), HTTP(S), and SSL/TLS. Security: Expertise in the security of Internet properties, including WAF, Bot Management, Firewalls, Rate Limiting, (M)TLS, Zero Trust, and SASE concepts. Performance: Expertise in the performance of Internet properties, including speed, latency, caching, video streaming, HTTP/2, and TLS 1.3. AI Proficiency: Familiarity with prompt engineering, AI-assisted scripting and log analysis, LLM utilization, and workflow automation to augment everyday CE workflows. Coding/Scripting: Proficiency in Python, JavaScript, Bash, Lua, Go, or similar languages to write custom integration scripts, API connectors, and edge computing functions. Developer Platform Fluency: Experience designing, building, deploying or scaling applications using RESTful/GraphQL APIs, SDKs, webhooks, and developer tooling in cloud-native environments. Familiarity with infrastructure-as-code (Terraform, Pulumi), CI/CD pipelines, and observability for developer-led deployments. Strongly Preferred Qualifications (The "Good-to-Haves") Technical Enablement: Track record of creating and delivering technical enablement - workshops, code samples, documentation, or developer onboarding - that accelerates customer adoption of complex platforms. Applied AI Knowledge: Experience with LLM orchestration, Retrieval-Augmented Generation (RAG) architectures, and model-access controls. Regulatory Knowledge: Understanding of regulatory requirements such as PCI DSS, HIPAA, and SOC 2. Hyperscaler / Digital Native Background: Talent coming out of hyperscalers, digital natives, distributed systems environments, or startups is strongly valued for its deeper cloud fluency and advanced architecture depth. Education Undergraduate or graduate degree in Computer Science, Engineering, or a related field. Travel This role may require travel up to 20–50% to attend customer meetings, conferences, industry events, and in-person team collaboration. Compensation This role features a competitive base salary and a variable compensation structure tied directly to the successful execution of full-lifecycle commercial targets, including new business acquisition, ongoing adoption milestones, and overall account expansion. Compensation may be adjusted depending on work location. For New York City based hires: Estimated annual salary of $204,000.00 - $321,000.00 Equity This role is eligible to participate in Cloudflare’s equity plan. Benefits Cloudflare offers a complete package of benefits and programs to support you and your family.  Our benefits programs can help you pay health care expenses, support caregiving, build capital for the future and make life a little easier and fun!  The below is a description of our benefits for employees in the United States, and benefits may vary for employees based outside the U.S. Health & Welfare Benefits Medical/Rx Insurance Dental Insurance Vision Insurance Flexible Spending Accounts Commuter Spending Accounts Fertility & Family Forming Benefits On-demand mental health support and Employee Assistance Program Global Travel Medical Insurance Financial Benefits Short and Long Term Disability Insurance Life & Accident Insurance 401(k) Retirement Savings Plan Employee Stock Participation Plan Time Off Flexible paid time off covering vacation and sick leave Leave programs, including parental, pregnancy health, medical, and bereavement leave What Makes Cloudflare Special? We’re not just a highly ambitious, large-scale technology company. We’re a highly ambitious, large-scale technology company with a soul. Fundamental to our mission to help build a better Internet is protecting the free and open Internet. Project Galileo : Since 2014, we've equipped more than 2,400 journalism and civil society organizations in 111 countries with powerful tools to defend themselves against attacks that would otherwise censor their work, technology already used by Cloudflare’s enterprise customers--at no cost. Athenian Project : In 2017, we created the Athenian Project to ensure that state and local governments have the highest level of protection and reliability for free, so that their constituents have access to election information and voter registration. Since the project, we've provided services to more than 425 local government election websites in 33 states. 1.1.1.1 : We released 1.1.1.1 to help fix the foundation of the Internet by building a faster, more secure and privacy-centric public DNS resolver. This is available publicly for everyone to use - it is the first consumer-focused service Cloudflare has ever released. Here’s the deal - we don’t store client IP addresses never, ever. We will continue to abide by our privacy commitment and ensure that no user data is sold to advertisers or used to target consumers. Sound like something you’d like to be a part of? We’d love to hear from you! Please note that applicants who progress to the offer stage of the interview process may be asked to attend an in-person interview within one of the Cloudflare Offices or Cloudflare Hubs.  More details about this will be available at that stage of the interview process. This position may require access to information protected under U.S. export control laws, including the U.S. Export Administration Regulations. Please note that any offer of employment may be conditioned on your authorization to receive software or technology controlled under these U.S. export laws without sponsorship for an export license. Cloudflare is proud to be an equal opportunity employer.  We are committed to providing equal employment opportunity for all people and place great value in both diversity and inclusiveness.  All qualified applicants will be considered for employment without regard to their, or any other person's, perceived or actual race, color, religion, sex, gender, gender identity, gender expression, sexual orientation, national origin, ancestry, citizenship, age, physical or mental disability, medical condition, family care status, or any other basis protected by law. We are an AA/Veterans/Disabled Employer. Cloudflare provides reasonable accommodations to qualified individuals with disabilities.  Please tell us if you require a reasonable accommodation to apply for a job. Examples of reasonable accommodations include, but are not limited to, changing the application process, providing documents in an alternate format, using a sign language interpreter, or using specialized equipment.  If you require a reasonable accommodation to apply for a job, please contact us via e-mail at hr@cloudflare.com or via mail at 101 Townsend St. San Francisco, CA 94107.

Location: Санкт-Петербург SberData отвечает за развитие платформы для работы с данными в Сбере. У нас свыше 230 Петабайт данных, около сотни продуктов и сервисов для клиентов. Мы создаем собственные сборки платформ в рамках вендорозамещения и активно сотрудничаем со всеми блоками Сбера для решения их бизнес-задач: наши пользователи сделали уже свыше 6500 витрин. SberData внедряет лучшие практики работы с данными, снижает стоимость хранения данных, повышает скорость получения данных, внедряет AI-технологии. Нашими решениями пользуются свыше 30 тыс человек. Технологический стек: Python, pydantic, FastMCP; RAG/поиск: ChromaDB, эмбеддинги, rank-bm25; наблюдаемость: OpenSearch (DSL), Grafana API (PromQL); платформа как объект управления: Kafka, Flink, Spark, HDFS, OpenAPI-спеки; инфраструктура: Git (Bitbucket), Jenkins, Nexus, Vault, k8s. Обязанности ### Задачи * разработка MCP-инструментов: Python/FastMCP * развитие RAG-контура базы знаний: индексация, гибридный поиск (dense-эмбеддинги + BM25), оценка качества, reranker * аналитика данных платформы * код-ревью, документирование решений, развитие тестов (pytest). Требования ### Требования * высшее образование: техническое/математическое * опыт разработки на Python не менее 1,5 лет * базовое понимание области data engineering: как устроены ETL/репликация данных, потоковые пайплайны * SQL — уверенный уровень * Git, CI/CD (Jenkins или аналог) * Linux/bash * английский — чтение технической документации. ### Будет плюсом * опыт построения RAG: векторные БД, эмбеддинги, чанкинг, оценка качества поиска (hit rate/recall/MRR) * навыки работы с генеративными AI-моделями, опыт создания AI-агентов (MCP, LangChain) * знакомство с Kafka, Flink/Spark, Hadoop/HDFS * опыт интеграций по REST/OpenAPI * опыт Java/Scala. Условия ### **Мы предлагаем** * комфортный современный офис в г. Санкт-Петербург ул Уральская, 1литерВ * возможность выбрать удобный график – офис/гибрид * ежегодный пересмотр зарплаты, годовая премия * корпоративный спортзал и зоны отдыха * более 400 образовательных программ СберУниверситета для профессионального и карьерного развития * расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа * гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ * подписка Прайм с возможностью совместного использования на трёх близких * вознаграждение за рекомендацию друзей в команду Сбера.

Location: United States, San Diego, CA Why Sony Interactive Entertainment? Sony Interactive Entertainment isn’t just the Best Place to Play — it’s also the Best Place to Work. Sony Interactive Entertainment (SIE) is the company behind the PlayStation brand. As a subsidiary of Sony Group Corporation, we’re part of a proud legacy of innovation and excellence. SIE is a dynamic technology company, delivering cutting-edge hardware and network services to more than 100 million people and an entertainment leader, home to some of the most beloved and recognizable intellectual properties (IP) in the world. Our role at SIE is to create and nurture the experiences under the PlayStation brand, a name synonymous with entertainment excellence and creativity. SDET Contractor We are looking for an experienced Software Development Engineer in Test (SDET) to help ensure the quality, reliability, and resilience of Agentic AI systems and backend services. This role will design and implement automated testing strategies across AI/LLM-driven applications, APIs, microservices, and distributed systems. The ideal candidate brings strong Quality Engineering and test automation fundamentals, with experience testing Generative AI, LLM-based, or Agentic AI systems. They should be comfortable testing complex systems where behavior may be non-deterministic and traditional pass/fail testing alone may not be sufficient. Responsibilities Develop and execute comprehensive QE strategies and test plans for Agentic AI systems, backend services, APIs, and service integrations. Design and maintain scalable automated test frameworks across API, functional, integration, contract, regression, and end-to-end testing. Develop testing approaches for non-deterministic AI behavior and establish meaningful evaluation criteria. Identify quality risks early and apply risk-based, shift-left testing practices throughout the development lifecycle. Integrate automated testing into CI/CD pipelines and establish effective quality gates. Partner with Software Engineers, Product Managers, and other QE teams to improve testability, observability, and overall product quality. Analyze defects and production issues to identify root causes, testing gaps, and opportunities for continuous QE improvement. Establish quality metrics and provide visibility into test coverage, reliability, defects, and release readiness. What You'll Do Test Agentic AI workflows, including multi-step execution, tool selection and invocation, task completion, and failure handling. Validate AI-generated responses using appropriate measures for accuracy, relevance, groundedness, consistency, and adherence to expected behavior. Test AI guardrails, unexpected inputs, and failure scenarios, including incorrect tool usage and prompt-based attacks. Validate interactions between AI agents, APIs, tools, data sources, and downstream services. Develop reusable automation, test data, and evaluation scenarios for repeatable AI and services testing. Validate API contracts, schemas, authentication, business rules, error handling, and service integrations. Investigate failures across distributed systems and distinguish application, integration, environmental, automation, and AI-related issues. Participate in defect triage, root-cause analysis, regression testing, and release-readiness activities. Qualifications Bachelor's degree in Computer Science, Engineering, Information Technology, or a related field, or equivalent practical experience. 4+ years of experience in Software Development Engineering in Test, Quality Engineering, Software Engineering, or a related discipline. Strong experience testing REST APIs, backend services, microservices, or distributed systems. Hands-on experience developing automated tests using Python, Java, JavaScript, TypeScript, or a similar programming language. Experience with functional, integration, regression, contract, and end-to-end testing. Experience integrating automated tests into CI/CD pipelines. Strong analytical, debugging, and root-cause-analysis skills. Experience working in an Agile development environment and collaborating across engineering teams. Required Skills Strong understanding of software testing methodologies and Quality Engineering practices. Strong API and services testing experience, including REST, JSON, schemas, authentication, and error handling. Experience designing and maintaining automated test frameworks. Experience with Git and CI/CD development workflows. Ability to develop risk-based test strategies and identify appropriate testing layers. Experience testing distributed systems and service-to-service integrations. Hands-on experience testing Generative AI, LLM-powered applications, or Agentic AI systems. Understanding of testing non-deterministic AI behavior and evaluating outputs beyond exact-match assertions. Preferred Skills (Good to have) Experience testing Agentic AI workflows, including planning, multi-step execution, tool calling, memory, or state management. Experience with AI evaluation techniques such as evaluation datasets, semantic evaluation, LLM-as-a-Judge, groundedness, relevance, or hallucination detection. Experience with AI/LLM evaluation or observability tools such as LangSmith, DeepEval, Ragas, Arize Phoenix, Promptfoo, or similar technologies. Experience testing RAG systems or AI agents interacting with APIs, databases, or external tools. Experience with contract testing tools such as Pact and API specifications such as OpenAPI/Swagger. Experience with event-driven architectures, Kafka, Docker, Kubernetes, or cloud platforms such as AWS. Experience with performance, scalability, reliability, or resiliency testing. Experience establishing QE metrics, quality gates, or cross-team testing standards. Sony is an Equal Opportunity Employer. All persons will receive consideration for employment without regard to race, color, religion, gender, pregnancy, national origin, ancestry, citizenship, age, legally protected physical or mental disability, covered veteran status, status in the U.S. uniformed services, sexual orientation, marital status, genetic information or membership in any other legally protected category. We strive to create an inclusive environment, empower employees and embrace diversity. We encourage everyone to respond. We sincerely appreciate the time and effort you spent in contacting us and we thank you for your interest in PlayStation. At SIE, working with our partners, we consider several factors when setting each role’s base pay range, including the competitive benchmarking data for the market and geographic location. Please note that the individual base pay range may vary based on job-related factors which may include knowledge, skills, experience, and location. In addition, this role is eligible for benefit offerings that include medical, dental, and vision. Click here to learn more. The estimated base pay range for this role is listed below, this is an hourly rate. $58 — $72 USD Please note, Sony Interactive Entertainment conducts background checks at the offer stage for all new employees (which may include criminal background checks for some roles) and will need to process personal information to support these checks. Please refer to our Candidate Privacy Notice (where applicable) for more information about what personal information we collect, how we use it, who we share it with, and your data protection rights. Equal Opportunity Statement: Sony is an Equal Opportunity Employer. All persons will receive consideration for employment without regard to gender (including gender identity, gender expression and gender reassignment), race (including colour, nationality, ethnic or national origin), religion or belief, marital or civil partnership status, disability, age, sexual orientation, pregnancy, maternity or parental status, trade union membership or membership in any other legally protected category. We strive to create an inclusive environment, empower employees and embrace diversity. We encourage everyone to respond. Sony Interactive Entertainment is a Fair Chance employer and qualified applicants with arrest and conviction records will be considered for employment.

Location: United States, Aliso Viejo, CA Why Sony Interactive Entertainment? Sony Interactive Entertainment isn’t just the Best Place to Play — it’s also the Best Place to Work. Sony Interactive Entertainment (SIE) is the company behind the PlayStation brand. As a subsidiary of Sony Group Corporation, we’re part of a proud legacy of innovation and excellence. SIE is a dynamic technology company, delivering cutting-edge hardware and network services to more than 100 million people and an entertainment leader, home to some of the most beloved and recognizable intellectual properties (IP) in the world. Our role at SIE is to create and nurture the experiences under the PlayStation brand, a name synonymous with entertainment excellence and creativity. SDET Contractor We are looking for an experienced Software Development Engineer in Test (SDET) to help ensure the quality, reliability, and resilience of Agentic AI systems and backend services. This role will design and implement automated testing strategies across AI/LLM-driven applications, APIs, microservices, and distributed systems. The ideal candidate brings strong Quality Engineering and test automation fundamentals, with experience testing Generative AI, LLM-based, or Agentic AI systems. They should be comfortable testing complex systems where behavior may be non-deterministic and traditional pass/fail testing alone may not be sufficient. Responsibilities Develop and execute comprehensive QE strategies and test plans for Agentic AI systems, backend services, APIs, and service integrations. Design and maintain scalable automated test frameworks across API, functional, integration, contract, regression, and end-to-end testing. Develop testing approaches for non-deterministic AI behavior and establish meaningful evaluation criteria. Identify quality risks early and apply risk-based, shift-left testing practices throughout the development lifecycle. Integrate automated testing into CI/CD pipelines and establish effective quality gates. Partner with Software Engineers, Product Managers, and other QE teams to improve testability, observability, and overall product quality. Analyze defects and production issues to identify root causes, testing gaps, and opportunities for continuous QE improvement. Establish quality metrics and provide visibility into test coverage, reliability, defects, and release readiness. What You'll Do Test Agentic AI workflows, including multi-step execution, tool selection and invocation, task completion, and failure handling. Validate AI-generated responses using appropriate measures for accuracy, relevance, groundedness, consistency, and adherence to expected behavior. Test AI guardrails, unexpected inputs, and failure scenarios, including incorrect tool usage and prompt-based attacks. Validate interactions between AI agents, APIs, tools, data sources, and downstream services. Develop reusable automation, test data, and evaluation scenarios for repeatable AI and services testing. Validate API contracts, schemas, authentication, business rules, error handling, and service integrations. Investigate failures across distributed systems and distinguish application, integration, environmental, automation, and AI-related issues. Participate in defect triage, root-cause analysis, regression testing, and release-readiness activities. Qualifications Bachelor's degree in Computer Science, Engineering, Information Technology, or a related field, or equivalent practical experience. 4+ years of experience in Software Development Engineering in Test, Quality Engineering, Software Engineering, or a related discipline. Strong experience testing REST APIs, backend services, microservices, or distributed systems. Hands-on experience developing automated tests using Python, Java, JavaScript, TypeScript, or a similar programming language. Experience with functional, integration, regression, contract, and end-to-end testing. Experience integrating automated tests into CI/CD pipelines. Strong analytical, debugging, and root-cause-analysis skills. Experience working in an Agile development environment and collaborating across engineering teams. Required Skills Strong understanding of software testing methodologies and Quality Engineering practices. Strong API and services testing experience, including REST, JSON, schemas, authentication, and error handling. Experience designing and maintaining automated test frameworks. Experience with Git and CI/CD development workflows. Ability to develop risk-based test strategies and identify appropriate testing layers. Experience testing distributed systems and service-to-service integrations. Hands-on experience testing Generative AI, LLM-powered applications, or Agentic AI systems. Understanding of testing non-deterministic AI behavior and evaluating outputs beyond exact-match assertions. Preferred Skills (Good to have) Experience testing Agentic AI workflows, including planning, multi-step execution, tool calling, memory, or state management. Experience with AI evaluation techniques such as evaluation datasets, semantic evaluation, LLM-as-a-Judge, groundedness, relevance, or hallucination detection. Experience with AI/LLM evaluation or observability tools such as LangSmith, DeepEval, Ragas, Arize Phoenix, Promptfoo, or similar technologies. Experience testing RAG systems or AI agents interacting with APIs, databases, or external tools. Experience with contract testing tools such as Pact and API specifications such as OpenAPI/Swagger. Experience with event-driven architectures, Kafka, Docker, Kubernetes, or cloud platforms such as AWS. Experience with performance, scalability, reliability, or resiliency testing. Experience establishing QE metrics, quality gates, or cross-team testing standards. Sony is an Equal Opportunity Employer. All persons will receive consideration for employment without regard to race, color, religion, gender, pregnancy, national origin, ancestry, citizenship, age, legally protected physical or mental disability, covered veteran status, status in the U.S. uniformed services, sexual orientation, marital status, genetic information or membership in any other legally protected category. We strive to create an inclusive environment, empower employees and embrace diversity. We encourage everyone to respond. We sincerely appreciate the time and effort you spent in contacting us and we thank you for your interest in PlayStation. At SIE, working with our partners, we consider several factors when setting each role’s base pay range, including the competitive benchmarking data for the market and geographic location. Please note that the individual base pay range may vary based on job-related factors which may include knowledge, skills, experience, and location. In addition, this role is eligible for benefit offerings that include medical, dental, and vision. Click here to learn more. The estimated base pay range for this role is listed below, this is an hourly rate. $58 — $72 USD Please note, Sony Interactive Entertainment conducts background checks at the offer stage for all new employees (which may include criminal background checks for some roles) and will need to process personal information to support these checks. Please refer to our Candidate Privacy Notice (where applicable) for more information about what personal information we collect, how we use it, who we share it with, and your data protection rights. Equal Opportunity Statement: Sony is an Equal Opportunity Employer. All persons will receive consideration for employment without regard to gender (including gender identity, gender expression and gender reassignment), race (including colour, nationality, ethnic or national origin), religion or belief, marital or civil partnership status, disability, age, sexual orientation, pregnancy, maternity or parental status, trade union membership or membership in any other legally protected category. We strive to create an inclusive environment, empower employees and embrace diversity. We encourage everyone to respond. Sony Interactive Entertainment is a Fair Chance employer and qualified applicants with arrest and conviction records will be considered for employment.

Проектировать и развивать LLM-пайплайны для ИИ-помощника: диалоги, уточняющие вопросы, рекомендации, саммари, инсайты, объяснения медицинских данных простым языком. Разрабатывать агентные сценарии: сбор данных из внешних источников, валидация данных, обогащение профиля, уточнение рисков, подготовка данных к приёму врача, контроль следования рекомендациям.

Workplace: Удаленно RWB — международная технологическая группа компаний, образованная в результате слияния двух лидеров рынка — IT-компании Wildberries и оператора наружной рекламы Russ. Мы развиваем одну из крупнейших цифровых экосистем, объединяя электронную коммерцию, финтех, логистику, рекламу, путешествия, здоровье и другие направления. Наши технологии помогают людям решать повседневные задачи, а предпринимателям — находить новые возможности для роста. Мы - команда, которая занимается разработкой и внедрением прикладных ML-решений на основе пользовательского контента (отзывы, тексты товаров, речь). Ключевые направления: генеративные модели, классификация, RAG-системы. Мы ищем универсального ML-инженера с уклоном в LLM/NLP, способного работать на всех этапах - от исследования до продакшена. Обязанности Дообучать и кастомизировать LLM под бизнес-задачи: SFT, RLHF; Строить и оптимизировать RAG-системы: работа с чанкингом, векторными индексами (FAISS/pgvector/Qdrant), реранкингом; Разрабатывать пайплайны оценки моделей, включая LLM-as-a-Judge и бенчмарки; Участвовать в создании агентных систем и деплое моделей (Triton/vLLM/TGI); Самостоятельно работать с данными: SQL, Pandas, выборка и подготовка данных. Требования Коммеческий опыт работы в Data Science от года; Практические навыки fine-tuning LLM и настройки RAG-пайплайнов; Уверенное владение Python и знание базовых алгоритмов и структур данных; Понимание типичных проблем ML-систем (например, галюцинациии генеративных моделей, смещение данных). Будет плюсом: Опыт развертывания моделей на собственном железе; Навыки построения агентных систем или бенчмарков для оценки LLM. Условия Обучение и развитие: языковые клубы, собственный корпоративный университет, программы развития управленческих навыков и многое другое; Благополучие сотрудников: корпоративный пакет ДМС со стоматологией, корпоративный спорт, консультации психолога и дополнительные возможности аккредитованной IT-компании; Множество сообществ: клуб спикеров, футбола, йоги, шахмат и т.д.; Забота о семьях: создаем условия, в которых легко сочетать карьеру и заботу о близких – от гибкого подхода до масштабных проектов для детей сотрудников; Скидки и партнерские программы: на обучение, страхование, покупки и многое другое; Комфортная рабочая среда: бесплатное питание в офисе, современные офисы рядом с метро, корпоративная техника и портал для сотрудников.

Location: Москва В кластере мониторинга корпоративного портфеля Блока "Риски" открыта вакансия Senior DS (LLM & AI Agents). Ищем специалиста, способного проектировать эффективные LLM-решения для мониторинга кредитных рисков средних и крупных компаний в портфеле Сбера, формулировать и тестировать гипотезы их улучшения, разработать систему модельных и бизнес-метрик, писать код для внедрения в промышленный процесс. Обязанности * разработка мультиагентной системы мониторинга корпоративных клиентов * построение llm evals suite для контроля качества работы системы * построение RAG: сбор данных, настройка hybrid search, reranking * интеграция с другими агентами (A2A) и сервисами. Требования * опыт разработки и внедрения LLM-based решений и AI-агентов: от прототипирования до промышленной эксплуатации * опыт разработки в фреймворках: LangChain, LangGraph * опыт решения прикладных задач с использованием RAG, tool calling, structured output, multi-step reasoning и agentic workflows, включая интеграцию с корпоративными данными и сервисами * опыт применения асинхронного и многопоточного программирования (aiohttp, asyncio, multiprocessing) * знание классического ML (scikit-learn, pandas, scipy), основ математической статистики * владение git, SQL, bash. **Будет плюсом**: * опыт работы в финансовых организация и знание доменной области (кредитные риски) * опыт работы с Kubernetes, понимание принципов MLOps и CI/CD. Условия * работа в удобном и современном офисе недалеко от станции метро Кутузовская * ежегодный пересмотр зарплаты, годовая премия * корпоративный спортзал и зоны отдыха * более 400 образовательных программ СберУниверситета для профессионального и карьерного развития * расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа * гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ * подписка Прайм с возможностью совместного использования на трёх близких * вознаграждение за рекомендацию друзей в команду Сбера.

Развитие HR-платформы и внутренних сервисов. От 4 лет коммерческой разработки, из них от 2 лет на Go.

Location: Москва Ищем разработчика, который пишет промышленный код и умеет системно использовать LLM и AI‑агентов в работе: от декомпозиции задач до приёмки результата и построения интерфейсов для управления агентами. Обязанности * разработка и развитие backend‑сервисов на Python (REST API, интеграции с внешними сервисами и LLM) * проектирование и разработка AI‑агентов: цели, планирование, инструменты, оркестрация нескольких агентов * создание интерфейсов для управления AI‑агентами: дашборды, конфигурация, логирование, отладка * использование LLM в разработке: генерация кода, рефакторинг, тесты, документация * декомпозиция задач для моделей и агентов, проверка результата, настройка промптов и пайплайнов * контроль качества ответов моделей (поиск галлюцинаций, валидация, защитные контуры) * работа с очередями и событиями через Kafka * написание автотестов, базовая observability (логирование, метрики). Требования * опыт работы с несколькими LLM (OpenAI / Anthropic / Mistral / Qwen / Llama / Gemini и т.п.) и понимание, где какую применять * понимание базовых концепций: агенты, tools/functions, RAG, параметры генерации, контекстное окно * умение выявлять галлюцинации и строить проверки (cross‑check, валидация на данных) * опыт интеграции LLM в backend (API‑вызовы, ретраи, лимиты, логирование промптов/ответов) * отличное знание Python, стандартные библиотеки, типизации (typing/pydantic) * опыт разработки промышленных backend‑систем (прод, нагрузка или сложная доменная логика) * опыт проектирования и разработки REST API (FastAPI / Django / Flask) * обязательный опыт работы с Kafka (продюсеры, консюмеры, топики, обработка событий) * понимание асинхронности в Python (asyncio) и её практическое применение * опыт разработки интерфейсов для внутренних инструментов/админок/дашбордов * базовое понимание UI/UX: понятные сценарии, визуализация состояния агентов и ошибок * опыт с одним из современных фронтенд‑стеков (React / Vue / др.) или уверенный HTML/CSS/JS и готовность работать с выбранным фреймворком. ### Будет плюсом: * опыт работы с OpenClaw и/или Ouroboros (многоагентные пайплайны, оркестрация) * собственные проекты с LLM/агентами, участие в open source. Условия * комфортный современный офис на м. Кутузовская * формат работы: офис на время адаптации и далее гибрид (2–3 дня в неделю в офисе) * ежегодный пересмотр зарплаты, годовая премия * корпоративный спортзал и зоны отдыха * более 400 образовательных программ СберУниверситета для профессионального и карьерного развития * расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа * подписка Прайм с возможностью совместного использования на трёх близких * вознаграждение за рекомендации друзей в команду Сбера.

Location: Москва Мы ищем не просто стажера, а будущего эксперта на стыке классической информационной безопасности и защиты искусственного интеллекта. В нашей команде ты не будешь варить кофе. Ты погрузишься в защиту периметра следующего поколения (NGFW), научишься строить «щиты» для языковых моделей (LLM Firewall) и разберешься, как защитить бизнес-данные в эпоху генеративного ИИ. Если ты понимаешь, как работает сеть, интересуешься тем, как «взламывают» нейросети, и хочешь учиться у лучших — тебе к нам. ### Обязанности Защита сетей и NGFW: * Помощь в администрировании NGFW: участие в настройке политик межсетевого экранирования и микросегментации. * Мониторинг сетевых аномалий и первичный анализ трафика уровня L7. * Помощь в интеграции событий ИБ со стеком мониторинга SOC. Эксплуатация LLM Firewall (AI Security): * Тестирование промежуточного слоя (Guardrails) между пользователями и LLM. * Генерация тестовых промптов для проверки на уязвимости (prompt injection, jailbreak). * Анализ ответов моделей на предмет утечек конфиденциальных данных из RAG-контекстов. Защита персональных данных: * Участие в задачах по обезличиванию и псевдонимизации данных (маскирование, декомпозиция). * Помощь в аудите цепочек обработки запросов на соответствие требованиям 152-ФЗ. Мониторинг и документирование: * Анализ алертов от SOC, помощь в расследовании инцидентов ИБ. * Актуализация технической документации: рисование схем потоков данных, ведение реестров конфигураций. * Помощь в проверке резервных копий и планировании обновлений СЗИ. Требования * Образование: Студент старших курсов или недавний выпускник (Информационная безопасность, ИТ, Прикладная математика, Computer Science). * Сети: Уверенное понимание модели OSI и стека TCP/IP, базовое понимание принципов маршрутизации. * AI-бэкграунд: Понимание базовых принципов работы LLM (что такое промпт, контекст, RAG, галлюцинации). Интерес к теме AI Security. * Законодательство: Базовое представление о том, что такое персональные данные и 152-ФЗ (или высокая готовность быстро в этом разобраться). * Soft skills: Внимательность к деталям, аналитический склад ума, умение гуглить и читать техническую документацию на английском языке. ### ### Будет преимуществом * Базовые навыки скриптинга на Python (написание простых парсеров, скриптов для автоматизации рутины). * Опыт работы с open-source LLM или инструментами класса guardrails (LiteLLM, NeMo Guardrails). * Участие в CTF-соревнованиях, хакатонах или наличие pet-проектов на стыке ИБ и AI. * Понимание методологий оценки уязвимостей (CVSS). * Инструментальное владение AI для решения рабочих и учебных задач. Условия * Современный офис в д. Аносино, ул. Университетская, вл. 11 (есть трансфер от м. 1905 года); * Льготные ипотечные условия кредитования; * Бесплатная подписка СберПрайм+, скидки на продукты компаний-партнеров: Okko, Сбер Маркет, Мега Маркет, Самокат, Еаптека и другие; * ДМС с первого дня и скидки на страхование для близких; * Корпоративная пенсионная программа; * Детский отдых и подарки за счет Компании; * Обучение за счет Компании: онлайн курсы, неограниченный доступ к библиотеке и обучение на базе Корпоративного университета, тренинги, митапы и возможность получить новую квалификацию.

👉🏻 будувати й підтримувати data pipelines; 👉🏻 автоматизувати ingestion, transformation, validation та loading даних;