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במקום לעבור לבד על אלפי מודעות, Jobify מנתחת את קורות החיים שלך ומציגה לך רק משרות שבאמת מתאימות לך.
מעל 80,000 משרות • אלפי משרות חדשות בכל יום
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כבר העליתם קו"ח בעבר? כניסה
מלאה
30,000-45,000 ₪
we are a leading platform for Trust & Safety teams worldwide, leveraging cutting‑edge AI and world‑class expertise to protect users from the widest spectrum of online harms. By continuously collecting and analyzing data, our solutions ensure customers stay two steps ahead of bad actors in an ever‑changing threat landscape.
Were seeking an outstanding Backend Engineer with a Generative AI orientation to join our research team. We are building a new AI Platform from scratch to standardize AI development methods, ensure excellence and efficiency, and deliver robust, scalable, and compliant AI capabilities. This platform will handle GenAI models deployment, prompt management, data filtering, logging, and monitoring, centrally, so individuals can focus on their own business-specific logic.
Key Responsibilities
Build and maintain CI/CD pipelines that support fast, reliable integration and deployment of GenAI systems across complex environments, including orchestrating multiple models/agents and MCP clients/servers.
Develop, deploy and integrate AI solutions and agentic frameworks, creating both command‑line tools and interactive notebooks that support our teams research and workflow automation needs.
Build and manage data pipelines and databases (SQL) for large‑scale data handling, automating data collection and curation (e.g., adversarial prompts for model red-teaming) to support AI training and evaluation.
Integrate research into production by implementing the latest generative AI advancements (including white‑box LLM development), integrating different models and agents, and developing evaluation frameworks (e.g., agentic reasoning tests) as part of our automated workflows.
Integration & Orchestration: Integrate dockerized environments (e.g., Harbor format) into internal training frameworks, optimizing reset/statefulness semantics, concurrency, and throughput ceilings
Deploy GenAI models from repositories such as HuggingFace onto our AWS platform, connect them to data pipelines, and automate end‑to‑end processes for prompt generation, labeling, and response evaluation.
Were seeking an outstanding Backend Engineer with a Generative AI orientation to join our research team. We are building a new AI Platform from scratch to standardize AI development methods, ensure excellence and efficiency, and deliver robust, scalable, and compliant AI capabilities. This platform will handle GenAI models deployment, prompt management, data filtering, logging, and monitoring, centrally, so individuals can focus on their own business-specific logic.
Key Responsibilities
Build and maintain CI/CD pipelines that support fast, reliable integration and deployment of GenAI systems across complex environments, including orchestrating multiple models/agents and MCP clients/servers.
Develop, deploy and integrate AI solutions and agentic frameworks, creating both command‑line tools and interactive notebooks that support our teams research and workflow automation needs.
Build and manage data pipelines and databases (SQL) for large‑scale data handling, automating data collection and curation (e.g., adversarial prompts for model red-teaming) to support AI training and evaluation.
Integrate research into production by implementing the latest generative AI advancements (including white‑box LLM development), integrating different models and agents, and developing evaluation frameworks (e.g., agentic reasoning tests) as part of our automated workflows.
Integration & Orchestration: Integrate dockerized environments (e.g., Harbor format) into internal training frameworks, optimizing reset/statefulness semantics, concurrency, and throughput ceilings
Deploy GenAI models from repositories such as HuggingFace onto our AWS platform, connect them to data pipelines, and automate end‑to‑end processes for prompt generation, labeling, and response evaluation.
Requirements:
Must-to-have
B.Sc. in Computer Science, Computer Engineering, or a related field (or equivalent hands‑on experience).
5+ years of experience delivering large‑scale, high‑performance systems on AWS, with an emphasis on orchestrating data pipelines and AI workflows in production environments.
Expertise in scripting and automation using Python and shell; proven ability to write clean IaC and CI/CD pipelines.
Strong understanding of Linux systems, networking, and distributed system design.
Ability to break down monolithic systems into scalable, loosely coupled services and microservices.
Strong cross‑functional communication and collaboration skills, with a DevOps mindset to drive best practices across teams.
Hands‑on experience deploying and integrating generative AI models (e.g., large language models or other AI/ML models) in production.
Hands‑on experience deploying MCP clients/servers and integrating them into automated AI workflows.
Nice-to-Have
M.Sc. in Computer Science, Computer Engineering, or a related field.
Experience with large‑scale cluster management or HPC scheduling tools (e.g., Slurm).
Knowledge of advanced observability and monitoring tools such as Prometheus and Grafana.
Experience with cloud cost optimization (FinOps concepts) and exposure to infrastructure security tools or configuration management for compliance.
Hands‑on experience working with A2A protocol.
Must-to-have
B.Sc. in Computer Science, Computer Engineering, or a related field (or equivalent hands‑on experience).
5+ years of experience delivering large‑scale, high‑performance systems on AWS, with an emphasis on orchestrating data pipelines and AI workflows in production environments.
Expertise in scripting and automation using Python and shell; proven ability to write clean IaC and CI/CD pipelines.
Strong understanding of Linux systems, networking, and distributed system design.
Ability to break down monolithic systems into scalable, loosely coupled services and microservices.
Strong cross‑functional communication and collaboration skills, with a DevOps mindset to drive best practices across teams.
Hands‑on experience deploying and integrating generative AI models (e.g., large language models or other AI/ML models) in production.
Hands‑on experience deploying MCP clients/servers and integrating them into automated AI workflows.
Nice-to-Have
M.Sc. in Computer Science, Computer Engineering, or a related field.
Experience with large‑scale cluster management or HPC scheduling tools (e.g., Slurm).
Knowledge of advanced observability and monitoring tools such as Prometheus and Grafana.
Experience with cloud cost optimization (FinOps concepts) and exposure to infrastructure security tools or configuration management for compliance.
Hands‑on experience working with A2A protocol.
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 80,000 משרות • אלפי משרות חדשות בכל יום
חינם. בלי פרסומות. בלי אותיות קטנות.
DOCX, DOC, PDF - בעברית או באנגלית
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