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Forward Deployed Engineer
Высокий балл обусловлен работой в перспективной сфере AI, солидным финансированием компании ($150M+) и возможностью напрямую влиять на успех клиентов. Однако необходимость частых поездок может подойти не всем.
Сложность вакансии
Роль требует редкого сочетания глубоких технических навыков (AI, системная интеграция, облачные вычисления) и отличных коммуникативных способностей для работы с клиентами. Необходимость частых командировок (до 30%) и работа на стыке разработки и консалтинга повышают уровень ответственности.
Анализ зарплаты
Зарплата в объявлении не указана, но для позиции Forward Deployed Engineer в Остине с опытом 3-5 лет рыночные показатели обычно выше среднего по рынку разработки ПО из-за клиентской составляющей. Ожидаемый диапазон составляет $140,000 - $180,000 в год.
Сопроводительное письмо
I am writing to express my strong interest in the Forward Deployed Engineer position at DevRev. With over 4 years of experience in software development and a deep focus on systems integration using TypeScript and Python, I have consistently delivered technical solutions that bridge the gap between complex platforms and specific customer needs. My background in building event-driven architectures and working with serverless environments like AWS Lambda aligns perfectly with your mission to unify data sources into an AI-ready platform.
In my previous roles, I have successfully balanced hands-on coding with customer-facing responsibilities, which has taught me how to translate business requirements into robust technical implementations. I am particularly excited about DevRev's vision of "Team Intelligence" and the opportunity to apply my knowledge of LLMs, RAG frameworks, and prompt engineering to help customers automate their workflows. I am eager to bring my expertise in API integrations and data analysis to the Applied AI Engineering team and contribute to the growth of your AI-native ecosystem.
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Откликнитесь в devrev уже сейчас
Присоединяйтесь к DevRev, чтобы внедрять инновационные AI-решения и трансформировать бизнес крупнейших мировых компаний!
Описание вакансии
About DevRev
At DevRev, we're building the future of work with Computer – your AI teammate. Unlike traditional tools, Computer unifies all your data sources, tools, and workflows into a single AI-ready platform, giving employees real-time insights, proactive suggestions, and powerful agentic actions. It extends your existing software with AI-native apps and agents that work alongside your teams and customers – updating workflows, coordinating across teams, and eliminating repetitive work. We call this Team Intelligence: human-AI collaboration that breaks down silos, brings people back together, and frees you to solve bigger problems. Backed by Khosla Ventures and Mayfield with $150M+ raised, DevRev is trusted by global companies across industries.
About the team
The Applied AI Engineering team ensures our customers get the optimal experience from DevRev. As customers go through their DevRev journey, they may identify needs for integration with existing enterprise systems and services, workflow and process automation, or customization of the DevRev platform to achieve their business objectives. Our team works with customers to understand requirements and design, develop, and implement solutions to meet customer goals.
Your mission is to systematically help customers find value with DevRev by developing a thorough understanding of their needs, owning coordination between internal and external stakeholders, and engineering the solution to get the job done. You are a product expert and will use your application development and AI/ML skills to ensure our customers get the most out of the DevRev platform.
About the role
As a Forward Deployed Engineer at DevRev, you will work closely with our post-sales Customer Experience team to help customers unlock value through AI. You will design and deploy intelligent agents, automated workflows, and integrations that transform how customers operate — connecting DevRev to their broader ecosystem and putting AI to work across their most important business processes. Where needed, you will build custom applications and solutions in TypeScript, JavaScript, Python, and related languages to bring these experiences to life.
You will employ your creativity and technical expertise to ideate and shape how AI, analytics, and workflows are used in customers’ processes. You are a product expert focused on implementation — building, shipping, and iterating to ensure customers get the most out of the DevRev platform.
What You’ll Do
- Customer-Facing: 30% working directly with customers
- Coding & Integration: 70–80% hands-on technical implementation
Build & Deploy
- Build & Deploy Solutions: Design, develop, and launch AI agents, integrations, and automations connecting DevRev with customer environments
- Integrate Systems: Connect DevRev with SaaS and non-SaaS platforms through APIs, webhooks, and real-time communication architectures for seamless data flow
- Optimize AI Performance: Apply prompt engineering, fine-tune semantic search engines, and leverage generative AI techniques to enhance agent accuracy and user experience
Data & Insights
- Own Data Insights: Write SQL queries, perform data analysis, and build dashboards to surface insights that drive customer decision-making
- Prototype & Iterate: Develop rapid proofs-of-concept, conduct live technical demos, and refine solutions based on customer and stakeholder feedback
Collaboration & Enablement
- Lead Cross-functional Collaboration: Maintain constant communication loops with customers, engineering, product, and customer success teams
- Guide Technical Adoption: Learn and master new tools, then guide customers through critical workflows
- Travel Ready: Willingness to travel up to 30% for on-site implementations, technical workshops, and customer engagements
What You Bring
- Experience: 3–5+ years in software development, systems integration, or platform engineering. Customer-facing experience is a plus
- Coding Skills: Strong proficiency in TypeScript, JavaScript, Python, data structures and algorithms.
- Applied AI Knowledge: Familiarity with LLMs, prompt engineering, frameworks like RAG and function calling; ability to build and configure AI agents
- Integration: Deep experience with API integration patterns (REST, GraphQL, webhooks), large-scale data synchronization, and event-driven/pub-sub architectures
- Cloud & Deployment: Hands-on experience deploying on serverless and edge platforms (AWS Lambda, Google Cloud Functions) with modern DevOps practices
- Data Transformation: Skilled in data mapping, schema alignment, and working with heterogeneous systems
- Observability: Experience implementing clear logging, actionable error surfacing, and telemetry to support faster debugging
- Communication: Strong written and verbal skills; ability to engage customers clearly and confidently
- Background: Bachelor’s or Master’s degree in Computer Science, Engineering, or related discipline
DevRev is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law.
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Навыки
- Data Analysis
- TypeScript
- Python
- LLM
- SQL
- JavaScript
- DevOps
- REST
- GraphQL
- RAG
- AWS Lambda
- Google Cloud Functions
Возможные вопросы на собеседовании
Роль предполагает 70-80% времени на написание кода и интеграции. Важно понимать опыт кандидата с современными API.
Расскажите о самом сложном проекте по интеграции систем, который вы реализовали. С какими проблемами синхронизации данных вы столкнулись и как их решили?
Вакансия сфокусирована на внедрении AI-агентов. Проверка практических навыков работы с LLM.
Как бы вы подошли к оптимизации точности AI-агента, использующего RAG, если он выдает нерелевантные ответы на специфические запросы клиента?
Позиция Forward Deployed подразумевает работу напрямую с заказчиком.
Опишите ситуацию, когда требования клиента технически противоречили возможностям платформы. Как вы управляли ожиданиями и какое решение предложили?
В описании указаны AWS Lambda и Google Cloud Functions.
Каковы основные преимущества и ограничения использования serverless-архитектуры при построении масштабируемых систем интеграции?
Упоминается работа с SQL и дашбордами.
Какие метрики вы бы использовали для оценки успешности внедрения нового AI-воркфлоу у клиента?
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