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במקום לעבור לבד על אלפי מודעות, Jobify מנתחת את קורות החיים שלך ומציגה לך רק משרות שבאמת מתאימות לך.
מעל 90,000 משרות • אלפי משרות חדשות בכל יום
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מלאה
22,000-32,000 ₪
Required Forward Deployed Engineer
The Role:
You are the hands-on engineer in a Forward-Deployed Engineering pod - a small team embedded with priority customers, owning technical delivery from first prototype to stable production. You build customer-specific capabilities that deliver immediate value and become durable platform capability when proven broadly useful.
Your pod never modifies the platform core. You consume the Foundation teams shared components - registry, gateway, evaluation harness, guardrails, memory - and extend them for your customers. Everything you ship is a versioned, tested engineering artifact held to platform-grade quality. When a skill you built for one customer proves useful across others, it is promoted into the foundation.
Agent engineering is the discipline: prompt design, software engineering, and evaluation - combined. You write the behavioral instructions, write the code beneath them, and prove both against evaluation and latency cases. You do this while embedded with the customer, translating their domain into working, tested artifacts.
Scope
For each customer engagement, your pod delivers:
Skills - versioned artifacts: behavioral instructions, executable code (scripts, tool wrappers, data transformations), domain knowledge, tone, and jargon handling. Registered through the platform registry with mandatory evaluation gates.
Integrations - wrapping and registering customer tools, APIs, and MCP servers with contracts and permissions. Bringing each through the Foundations gateway. Validating the full flow meets latency budgets - fixing and contributing back when it doesnt.
Evaluation suites - ground-truth scenarios and acceptance cases drawn from the customers domain. No skill passes the registry gate without them.
Speech assets - ASR vocabulary biasing (so the system recognizes customer brand names, products, acronyms) and TTS pronunciation lexicons (so the avatar speaks them correctly).
Ongoing tuning - knowledge creation, behavior configuration, checkups, and production feedback integration as the customers needs evolve.
The Role:
You are the hands-on engineer in a Forward-Deployed Engineering pod - a small team embedded with priority customers, owning technical delivery from first prototype to stable production. You build customer-specific capabilities that deliver immediate value and become durable platform capability when proven broadly useful.
Your pod never modifies the platform core. You consume the Foundation teams shared components - registry, gateway, evaluation harness, guardrails, memory - and extend them for your customers. Everything you ship is a versioned, tested engineering artifact held to platform-grade quality. When a skill you built for one customer proves useful across others, it is promoted into the foundation.
Agent engineering is the discipline: prompt design, software engineering, and evaluation - combined. You write the behavioral instructions, write the code beneath them, and prove both against evaluation and latency cases. You do this while embedded with the customer, translating their domain into working, tested artifacts.
Scope
For each customer engagement, your pod delivers:
Skills - versioned artifacts: behavioral instructions, executable code (scripts, tool wrappers, data transformations), domain knowledge, tone, and jargon handling. Registered through the platform registry with mandatory evaluation gates.
Integrations - wrapping and registering customer tools, APIs, and MCP servers with contracts and permissions. Bringing each through the Foundations gateway. Validating the full flow meets latency budgets - fixing and contributing back when it doesnt.
Evaluation suites - ground-truth scenarios and acceptance cases drawn from the customers domain. No skill passes the registry gate without them.
Speech assets - ASR vocabulary biasing (so the system recognizes customer brand names, products, acronyms) and TTS pronunciation lexicons (so the avatar speaks them correctly).
Ongoing tuning - knowledge creation, behavior configuration, checkups, and production feedback integration as the customers needs evolve.
Requirements:
4+ years in a technical, customer-facing engineering role - Forward Deployed Engineer, Solutions Engineer with production code responsibility, or Software Engineer with consulting/deployment experience. You write and review production-quality code, not just configure.
Production experience with LLMs - prompt engineering, agent development (LangChain, LangGraph, or equivalent), RAG pipelines, tool/function calling, evaluation frameworks, and deployment at scale. You stay current with the latest capabilities and patterns.
Ability to author and evaluate AI skills as engineering artifacts - writing behavioral instructions, writing the code beneath them, designing test scenarios that catch real failures, and measuring behavioral consistency across runs.
Experience building integrations that started as customer-specific and became broadly reusable - wrapping external APIs, webhooks, and tools into governed, contracted interfaces. Understanding latency across distributed calls.
Strong domain learning ability - you absorb unfamiliar industries (finance, education, media, telecom) quickly enough to author domain-specific artifacts within weeks. You conduct discovery well and translate domain conversations into engineering requirements.
Clear communication with engineers and non-technical stakeholders alike - you serve as the senior technical counterpart for customer teams, conduct architecture reviews, and spot risks early enough to act on them.
Ability to scope work, sequence delivery, and remove blockers - making deliberate trade-offs between scope, speed, and quality to protect delivery timelines in fast-moving or ambiguous environments.
4+ years in a technical, customer-facing engineering role - Forward Deployed Engineer, Solutions Engineer with production code responsibility, or Software Engineer with consulting/deployment experience. You write and review production-quality code, not just configure.
Production experience with LLMs - prompt engineering, agent development (LangChain, LangGraph, or equivalent), RAG pipelines, tool/function calling, evaluation frameworks, and deployment at scale. You stay current with the latest capabilities and patterns.
Ability to author and evaluate AI skills as engineering artifacts - writing behavioral instructions, writing the code beneath them, designing test scenarios that catch real failures, and measuring behavioral consistency across runs.
Experience building integrations that started as customer-specific and became broadly reusable - wrapping external APIs, webhooks, and tools into governed, contracted interfaces. Understanding latency across distributed calls.
Strong domain learning ability - you absorb unfamiliar industries (finance, education, media, telecom) quickly enough to author domain-specific artifacts within weeks. You conduct discovery well and translate domain conversations into engineering requirements.
Clear communication with engineers and non-technical stakeholders alike - you serve as the senior technical counterpart for customer teams, conduct architecture reviews, and spot risks early enough to act on them.
Ability to scope work, sequence delivery, and remove blockers - making deliberate trade-offs between scope, speed, and quality to protect delivery timelines in fast-moving or ambiguous environments.
This position is open to all candidates.
במקום לעבור לבד על אלפי מודעות, Jobify מנתחת את קורות החיים שלך ומציגה לך רק משרות שבאמת מתאימות לך.
מעל 90,000 משרות • אלפי משרות חדשות בכל יום
חינם. בלי פרסומות. בלי אותיות קטנות.
DOCX, DOC, PDF - בעברית או באנגלית
כבר העליתם קו"ח בעבר? כניסה
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