Senior AI Engineer

Tel Aviv · Hybrid | Full-Time | Cybersecurity

What You'll Do:

Design and develop LLM-powered security features and internal AI tools — RAG pipelines, multi-agent workflows, prompt-engineered systems for cybersecurity
Architect and operate multi-agent systems in production — orchestration, inter-agent communication, task delegation, failure handling at scale
Build agent monitoring and observability pipelines — tracing, drift/failure detection, alerting, reliability SLAs
Build and maintain scalable MLOps infrastructure — model serving, eval frameworks, experiment tracking, CI/CD for ML
Fine-tune and adapt foundation models on internal datasets (network telemetry, security logs, threat intel)
Establish best practices for model observability, safety, and responsible AI deployment
Stay current with the LLM/GenAI ecosystem; drive updates to the AI SDLC and AI Research cycle

tions 'from scratch'

Requirements:

Must-Have:

5–8 years SWE (2–3 in AI/ML)
Production LLM apps (RAG/agents/tool-use/fine-tuning)
Production multi-agent systems
Agent observability
LangChain/LangGraph/Bedrock AgentCore
Strong Python
MLOps pipelines
Transformers/embeddings/vector DBs
Cloud + K8s.

Nice-to-Have:

Cybersecurity background (significant plus)
Networking (SDN/BGP)
Model eval (LLM-as-judge/RAGAS)
MCP
Telecom/enterprise SaaS
publications/OSS in GenAI.

Stack:

Python, PyTorch, OpenAI/Anthropic APIs, LangChain, LangGraph, AWS Bedrock AgentCore, LangSmith, Kubernetes, Kafka, Elasticsearch, AWS, PostgreSQL