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GTM AI Engineer
Это перспективная позиция в известной кибербезопасной компании, работающей на стыке ИИ и бизнес-операций. Высокий балл обусловлен инновационностью задач и возможностью напрямую влиять на выручку компании через технологии.
Сложность вакансии
Роль требует уникального сочетания навыков: глубокого понимания бизнес-процессов (RevOps/GTM) и технической экспертизы в области LLM, интеграций и автоматизации. Кандидату необходимо иметь опыт работы с API и оркестраторами, такими как Workato или LangChain.
Анализ зарплаты
Зарплата в объявлении не указана, но для опытного инженера в Тель-Авиве рыночный диапазон составляет 35,000–45,000 ILS в месяц. Учитывая специфику AI и GTM, компенсация может быть выше среднего по рынку за счет бонусов и опционов.
Сопроводительное письмо
I am writing to express my strong interest in the GTM AI Engineer position at Transmit Security. With over 5 years of experience at the intersection of Business Operations and technical automation, I have a proven track record of building production-grade workflows that bridge the gap between complex data systems and revenue-driving teams. My background in integrating CRM platforms like Salesforce with advanced automation tools aligns perfectly with your mission to streamline GTM execution through AI.
In my previous roles, I have successfully deployed LLM-based solutions and AI agents that significantly reduced manual overhead for RevOps and Sales teams. I am particularly drawn to Transmit Security's focus on cybersecurity and identity journeys, and I am eager to apply my expertise in LangChain, Workato, and prompt engineering to enhance your xCIAM platform's internal efficiency. I am a high-ownership engineer who thrives on translating business needs into scalable, reliable technical solutions.
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Описание вакансии
Transmit Security gives businesses the modern tools they need to build secure, trusted and end-to-end digital identity journeys to innovate and grow.
CX-focused, cybersecurity conscious leaders rely on Transmit Security’s xCIAM platform to provide their customers with smooth experiences protected from fraud across all channels and devices.
Transmit Security serves many of the world’s largest banks, insurers, retailers, and other leading brands, collectively responsible for more than $1.3 trillion in annual commerce.
About the Role:
We are looking for a hands-on GTM AI Engineer to design, build, and deploy AI-powered workflows and automations across our go-to-market (GTM) systems.
This role sits at the intersection of Business Operations and technology, and acts as the engine behind revenue intelligence, process automation, and scalable pipeline generation systems.
You will translate business needs into production-grade AI solutions, including workflows, agents, and integrations, embedded directly into core business systems (e.g., CRM, marketing automation, support tools).
This is not a traditional software engineering role, but it requires strong technical execution and ownership across the full lifecycle.
What you’ll do:
- Design, build, and maintain production-grade AI workflows and automations to streamline GTM execution (e.g., lead enrichment, routing, research, follow-ups, CRM hygiene, and customer handoffs)
- Develop and deploy LLM-based solutions, including prompt-driven workflows, reusable prompt libraries, and AI agents embedded within business systems
- Build and maintain integrations and data pipelines across CRM, marketing automation, enrichment tools, and internal systems to ensure reliable, connected data across the funnel
- Monitor and continuously improve workflows and agents by optimizing accuracy, reliability, and business impact (e.g., precision/recall, failure handling, adoption)
- Identify opportunities to reduce manual work and improve efficiency across RevOps, Sales, Marketing, and Customer-facing teams
- Partner with cross-functional stakeholders to map processes, identify bottlenecks, and prioritize high-impact automation opportunities
- Drive adoption through enablement: documentation, training, and ongoing feedback loops
- Evaluate and implement new tools and technologies based on ROI, scalability, and maintainability
What you’ll need:
- 4–7 years of experience in Business Systems, RevOps systems, Data/Systems Engineering, or similar hands-on technical roles
- Proven experience building and deploying end-to-end workflows and automations in business environments
- Hands-on experience with LLM tools and prompt/workflow engineering, with a focus on reliability and real-world use cases
- Experience with automation/orchestration tools (e.g., workato, n8n, Make) and/or agent frameworks (e.g., LangChain or similar)
- Strong experience integrating and extending CRM systems (e.g., Salesforce), including workflows, data models, and system integrations
- Experience working with APIs, system integrations, and data flows across multiple platforms
- Experience supporting GTM / RevOps in a SaaS environment- Advantage
- Strong analytical and problem-solving skills, with the ability to prioritize based on business impact
- High ownership and ability to operate independently from idea to production
- Strong communication skills and ability to work effectively with both technical and non-technical stakeholders
#LI-AM1 #LI-Hybrid
#LI-TL1 #LI-Hybrid
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Навыки
- CRM
- Python
- LLM
- Salesforce
- Prompt Engineering
- APIs
- LangChain
- RevOps
- Make
- Workato
- n8n
Возможные вопросы на собеседовании
Проверка практического опыта внедрения ИИ в бизнес-процессы.
Расскажите о самом сложном AI-воркфлоу, который вы внедрили: какие инструменты использовали и как измеряли бизнес-результат?
Оценка навыков работы с CRM и понимания архитектуры данных.
Как вы обеспечиваете чистоту и синхронизацию данных в Salesforce при интеграции внешних AI-агентов через API?
Проверка умения работать с LLM в продакшене.
Какие стратегии вы используете для обработки галлюцинаций LLM и обеспечения надежности ответов в автоматизированных цепочках?
Оценка навыков приоритизации.
Как вы выбираете, какой процесс автоматизировать первым, если у вас есть запросы от отделов продаж, маркетинга и поддержки одновременно?
Проверка технического стека.
В чем, по вашему мнению, основные преимущества и недостатки использования No-code инструментов (Workato/Make) по сравнению с кастомным кодом на Python/LangChain для GTM задач?
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