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Forward Deployed Engineer (Perm, Ireland, Remote + Travel)
Отличная вакансия для опытных инженеров: работа с передовым стеком (AI/LLM), прозрачный процесс найма, щедрые бонусы на обучение и обустройство дома, а также возможность путешествовать по Европе.
Сложность вакансии
Высокая сложность обусловлена требованием к опыту (от 8 лет) и глубоким знаниям в узкой нише: разработке агентных AI-систем, оркестрации LLM и RAG. Также роль предполагает активный консалтинг и частые командировки по Европе.
Анализ зарплаты
Зарплата не указана, но для Senior-позиций в Ирландии в сфере AI/Cloud рыночный диапазон составляет €85,000–€115,000. Nearform известен конкурентными предложениями, соответствующими верхним границам рынка.
Сопроводительное письмо
I am writing to express my strong interest in the Forward Deployed Engineer position at Nearform. With over 8 years of experience in software engineering and a deep focus on building agentic platforms using Python, I am excited about the opportunity to help your clients operationalize LLM-powered agents. My background in architecting orchestration layers with LangGraph and implementing multi-agent systems aligns perfectly with Nearform’s mission to deliver measurable AI-driven results.
Throughout my career, I have developed a strong consulting mindset, successfully navigating complex enterprise environments and integrating AI solutions with legacy systems. I am particularly drawn to Nearform’s collaborative, people-first approach and your impressive track record with global leaders like Starbucks and Walmart. I am comfortable with the travel requirements and look forward to bringing my expertise in RAG pipelines and Kubernetes to your high-impact projects.
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Описание вакансии
Forward Deployed Engineer (Perm, Ireland, Remote + Travel)
This is a full-time, permanent opportunity for candidates based in Ireland.
This position also requires frequent travel to client sites within Europe, with travel expenses covered by the company.
About Nearform
Nearform is an independent team of data & AI experts, engineers, and designers who build intelligent digital solutions and capability at pace. We create AI-enabled solutions that enhance digital experiences, empower developers, and deliver measurable results. In these ways, we partner with ambitious enterprises to deliver enduring impact.
Our deep expertise in solving the world’s most complex digital problems, along with our collaborative, people-first approach, enables enterprises to build breakthrough products and modernise legacy systems by unleashing the power of AI.
Today, our team of 500 experts in 20+ countries is trusted by leading enterprises including Lululemon, Puma, Sun Life, Starbucks, Travelex, Virgin Media 02, and Walmart.
About the role
As a Forward Deployed Engineer, you will help build, implement, and deploy agentic platforms within client environments. Your role will focus on building and deploying scalable AI-driven solutions, enabling organisations to operationalise LLM-powered agents across products, processes, and teams. You will work closely with client stakeholders, technical leaders, and delivery teams to build, and roll out agentic capabilities that integrate seamlessly with existing enterprise systems and frameworks.
What we are looking for in this person:
- Significant experience delivering at a Senior Engineer level. Our Seniors typically have at least 8 years of commercial experience.
- Experience designing and building agentic platforms using Python and modern AI frameworks.
- Architect LLM orchestration using frameworks such as LangGraph, CrewAI, AutoGen, and related technologies.
- Implement multi-agent systems supporting tool use, workflow orchestration, memory, and decision-making.
- Design and deploy Retrieval-Augmented Generation (RAG) pipelines integrating enterprise data sources.
- Integrate agentic platforms with client systems, including APIs, data platforms, cloud services, and enterprise applications.
- The ability to configure Kubernetes and deploy workloads.
- Design deployment architectures across cloud, on-premise, and hybrid environments.
- Ability to collaborate efficiently in teams of technical and non-technical individuals.
- Comfort working in a dynamic environment with evolving objectives and direct iteration with users.
- Strong consulting experience to plan, execute, and deliver complex client engagements, solving real business problems.
Nice to have:
- Hands-on experience working with Golang
- Hands-on experience in the Frontend working with React
- Deep understanding of Kubernetes
Benefits & Perks:
- Annual Company Bonus - We all help Nearform to hit company goals so we all receive a share of the profits on an annual basis in line with company performance.
- Work Remotely and Flexibly - We have a genuine dedication to work/life balance. Our flexible working culture allows you to work around what matters - school run, no problem!
- Paid Time Off Package - We offer an annual leave of 24 days plus public holidays. We also offer sick leave, marriage leave and many more.
- Remote Working Allowance - Every 2 years, you will have a budget of up to €1250 (equivalent in GBP) to help you set up a comfortable and productive workspace in addition to your essential equipment provided by Nearform when you join.
- Training and Development Allowance - We understand the importance of continuously learning so we offer an allowance of up to €1000 (equivalent in GBP) you can use to upskill yourself.
- Healthcare - It’s important to always take care of your health, so we offer additional private healthcare here at Nearform. If you wish to learn more about the plan offered feel free to reach out to our team.
- Pension & Insurances - We offer a pension match of up to 5% and Income Protection and Death in Service for peace of mind.
Our Interview Process
- 1. Talent Call (30 mins via Zoom)
An introductory conversation with a member of our Talent Team to learn more about your background, experience, and career goals. We'll also provide a high-level overview of Nearform and answer any questions you may have. This step helps us assess initial alignment.
2. Technical Assessment (1 hour via Zoom)
A live coding challenge with one of our trained interviewers. You'll be asked to build a working solution to a problem via screen sharing, demonstrating your Python expertise, problem-solving ability, and coding best practices in a real-time environment.
3. Hiring Manager Interview (45 mins via Zoom)
A conversation with a Hiring Manager focused on your approach to teamwork, client collaboration, and problem-solving in an enterprise development setting. We'll also share more details about Nearform and the potential projects you may join.
There may also be an additional interview with the client depending on the project
Ready to Join Us?
If you're passionate about building high-quality applications, solving complex challenges, and driving innovation in enterprise development, we’d love to hear from you.
Apply now via our careers page at Nearform and be part of a team that values technical excellence, collaboration, and continuous learning.
Nearform is committed to shaping a better world in all that we do. Our global team is built based on respect, inclusivity, diversity and excellence.
If you require any adjustments to the interview process as part of your application, please let someone from our Talent Team know so they can help.
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Навыки
- Python
- LLM
- LangGraph
- CrewAI
- Autogen
- RAG
- Kubernetes
- Go
- React
- Cloud Computing
- API Integration
Возможные вопросы на собеседовании
Роль сфокусирована на создании агентных платформ, поэтому важно понимать опыт кандидата с конкретными инструментами.
Расскажите о вашем опыте проектирования многоагентных систем с использованием LangGraph или CrewAI. С какими основными сложностями вы сталкивались при управлении состоянием (state management)?
Forward Deployed Engineer работает напрямую с клиентами, где данные часто разрознены.
Как вы подходите к проектированию RAG-пайплайнов при интеграции с закрытыми корпоративными источниками данных?
Вакансия требует навыков настройки Kubernetes для деплоя AI-нагрузок.
Опишите ваш опыт развертывания LLM-приложений в Kubernetes. Какие стратегии масштабирования вы использовали для GPU-зависимых ворклоадов?
Это консалтинговая роль, требующая умения объяснять сложные вещи простыми словами.
Приведите пример ситуации, когда вам нужно было убедить технического лидера на стороне клиента изменить архитектурный подход. Как вы выстраивали аргументацию?
Работа предполагает частые итерации и меняющиеся требования.
Как вы приоритизируете задачи в условиях неопределенности, когда цели проекта на стороне клиента могут меняться в процессе реализации?
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