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Senior AI Engineer at DRIVENETS
Tel Aviv, Tel-Aviv District, Israel -
Full Time

Start Date

Immediate

Expiry Date

05 Sep, 26

Salary

0.0

Posted On

07 Jun, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

LLM, RAG Pipelines, Multi-agent Systems, Python, MLOps, LangChain, LangGraph, AWS Bedrock, Kubernetes, PyTorch, Vector Databases, Fine-tuning, Cybersecurity, Model Observability, Prompt Engineering, Transformers

Industry

Software Development

Description
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
Responsibilities
Design and develop LLM-powered security features, RAG pipelines, and multi-agent workflows for cybersecurity. Build and maintain scalable MLOps infrastructure, including model serving, observability pipelines, and fine-tuning of foundation models.