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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 • 4,000 חדשות ביום
חינם. בלי פרסומות. בלי אותיות קטנות.
מלאה
25,000-35,000 ₪
We are looking for a Senior AI Engineer to join our Cybersecurity team in Tel Aviv. You will design, build, and productionize LLM-powered applications, multi-agent systems, and MLOps infrastructure that power our company's next-generation cybersecurity capabilities. This is a high-impact, hands-on role at the intersection of applied AI, agentic systems, and network securit
What You'll Do
Design and develop LLM-powered security features and internal AI tools, including RAG pipelines, multi-agent workflows, and prompt-engineered systems tailored for cybersecurity use cases
Architect and operate multi-agent systems in production - including agent orchestration, inter-agent communication, task delegation, and failure handling at scale
Build robust agent monitoring and observability pipelines: tracing agent execution, detecting drift or failure, alerting on anomalous behavior, and maintaining agent reliability SLAs
Build and maintain scalable MLOps infrastructure: model serving, evaluation frameworks, experiment tracking, and CI/CD for ML models
Work with internal datasets (network telemetry, security logs, threat intelligence) to fine-tune and adapt foundation models for domain-specific detection and response tasks
Partner with the Cybersecurity, R&D, and infrastructure teams to define AI-driven security features and deliver them end-to-end
Establish best practices for model observability, safety, and responsible AI deployment within the organization
Stay current with the fast-moving LLM/GenAI and agentic AI ecosystem and evaluate emerging frameworks, models, and tools for adoption.
What You'll Do
Design and develop LLM-powered security features and internal AI tools, including RAG pipelines, multi-agent workflows, and prompt-engineered systems tailored for cybersecurity use cases
Architect and operate multi-agent systems in production - including agent orchestration, inter-agent communication, task delegation, and failure handling at scale
Build robust agent monitoring and observability pipelines: tracing agent execution, detecting drift or failure, alerting on anomalous behavior, and maintaining agent reliability SLAs
Build and maintain scalable MLOps infrastructure: model serving, evaluation frameworks, experiment tracking, and CI/CD for ML models
Work with internal datasets (network telemetry, security logs, threat intelligence) to fine-tune and adapt foundation models for domain-specific detection and response tasks
Partner with the Cybersecurity, R&D, and infrastructure teams to define AI-driven security features and deliver them end-to-end
Establish best practices for model observability, safety, and responsible AI deployment within the organization
Stay current with the fast-moving LLM/GenAI and agentic AI ecosystem and evaluate emerging frameworks, models, and tools for adoption.
Requirements:
Must-Have
5-8 years of software engineering experience, with at least 2-3 years focused on AI/ML engineering
Hands-on experience building production-grade LLM applications - RAG, agents, tool use, or fine-tuning
Proven experience designing and running multi-agent systems in production: orchestration patterns, agent state management, retries, and graceful degradation
Experience monitoring and observing AI agents in production - execution tracing, latency tracking, failure detection, and alerting (e.g., LangSmith, Arize, custom observability stacks)
Proficiency with agentic frameworks: LangChain, LangGraph, and/or AWS Bedrock AgentCore
Strong Python skills and comfort working across the full AI application stack
Experience designing and operating MLOps pipelines (model versioning, deployment, monitoring)
Solid understanding of transformer-based models, embeddings, and vector databases (e.g., Pinecone, Weaviate, pgvector)
Comfortable working in cloud environments (AWS, GCP, or Azure) and containerized deployments (Docker, Kubernetes)
Strong problem-solving skills and ability to work autonomously in a fast-paced environment
Nice-to-Have
Background in cybersecurity - threat detection, SIEM, SOC automation, or security data analysis - a significant plus for this role
Familiarity with networking concepts (SDN, cloud-native networking, BGP, telemetry)
Experience with model evaluation and benchmarking (LLM-as-judge, RAGAS, or custom eval harnesses)
Exposure to MCP (Model Context Protocol) for tool-augmented agentic workflows
Prior experience in enterprise SaaS, networking, or telecom domains
Publications, open-source contributions, or projects in the LLM/GenAI or agentic AI space
Our Stack
Python PyTorch OpenAI / Anthropic APIs LangChain LangGraph AWS Bedrock AgentCore LangSmith Kubernetes Kafka Elasticsearch AWS PostgreSQL GitHub Jira Confluence.
Must-Have
5-8 years of software engineering experience, with at least 2-3 years focused on AI/ML engineering
Hands-on experience building production-grade LLM applications - RAG, agents, tool use, or fine-tuning
Proven experience designing and running multi-agent systems in production: orchestration patterns, agent state management, retries, and graceful degradation
Experience monitoring and observing AI agents in production - execution tracing, latency tracking, failure detection, and alerting (e.g., LangSmith, Arize, custom observability stacks)
Proficiency with agentic frameworks: LangChain, LangGraph, and/or AWS Bedrock AgentCore
Strong Python skills and comfort working across the full AI application stack
Experience designing and operating MLOps pipelines (model versioning, deployment, monitoring)
Solid understanding of transformer-based models, embeddings, and vector databases (e.g., Pinecone, Weaviate, pgvector)
Comfortable working in cloud environments (AWS, GCP, or Azure) and containerized deployments (Docker, Kubernetes)
Strong problem-solving skills and ability to work autonomously in a fast-paced environment
Nice-to-Have
Background in cybersecurity - threat detection, SIEM, SOC automation, or security data analysis - a significant plus for this role
Familiarity with networking concepts (SDN, cloud-native networking, BGP, telemetry)
Experience with model evaluation and benchmarking (LLM-as-judge, RAGAS, or custom eval harnesses)
Exposure to MCP (Model Context Protocol) for tool-augmented agentic workflows
Prior experience in enterprise SaaS, networking, or telecom domains
Publications, open-source contributions, or projects in the LLM/GenAI or agentic AI space
Our Stack
Python PyTorch OpenAI / Anthropic APIs LangChain LangGraph AWS Bedrock AgentCore LangSmith Kubernetes Kafka Elasticsearch AWS PostgreSQL GitHub Jira Confluence.
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 • 4,000 חדשות ביום
חינם. בלי פרסומות. בלי אותיות קטנות.
תל אביב - יפו
ערב
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