Senior AI Engineer

Description:

Tel Aviv · Hybrid | Full-Time | Cybersecurity

What You'll Do:

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

tions 'from scratch'

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