D
Senior AI Engineer
·Israel
HybridFull-timeAI EngineeringNetworkingAI InfrastructureTelecommunications
DriveNets is seeking a Senior AI Engineer to design and develop LLM-powered security features and internal AI tools, including RAG pipelines, multi-agent workflows, and prompt-engineered systems. Architect and operate multi-agent systems in production, build agent monitoring and observability pipelines, and maintain scalable MLOps infrastructure.
Responsibilities
- Design and develop LLM-powered security features and internal AI tools, including RAG pipelines, multi-agent workflows, and prompt-engineered systems for cybersecurity use cases
- Architect and operate multi-agent systems in production, covering orchestration, inter-agent communication, task delegation, and failure handling at scale
- Build agent monitoring and observability pipelines, including tracing, drift and failure detection, alerting, and reliability SLA management
- Build and maintain scalable MLOps infrastructure, including model serving, evaluation frameworks, experiment tracking, and CI/CD for ML
- Fine-tune and adapt foundation models on internal datasets such as network telemetry, security logs, and threat intelligence
- Establish and champion best practices for model observability, safety, and responsible AI deployment
- Stay current with the LLM/GenAI ecosystem and drive continuous improvements to the AI SDLC and AI Research cycle
Requirements
- 5–8 years of software engineering experience, with 2–3 years focused on AI/ML
- Proven experience building and deploying production LLM applications (RAG, agents, tool-use, fine-tuning)
- Hands-on experience designing and operating production multi-agent systems
- Experience building agent observability and monitoring solutions
- Proficiency with LLM orchestration frameworks: LangChain, LangGraph, and/or AWS Bedrock AgentCore
- Strong Python programming skills
- Experience building and maintaining MLOps pipelines (model serving, eval frameworks, experiment tracking)
- Solid understanding of transformers, embeddings, and vector databases
- Experience with cloud infrastructure and Kubernetes
- Self-driven and proactive, able to establish best practices and drive initiatives independently
- Continuous learner who stays current with a rapidly evolving field and translates new knowledge into practical improvements
- Strong collaborator who works effectively across R&D and product teams
Nice to have
- Cybersecurity background (significant advantage)
- Networking domain knowledge (SDN, BGP)
- Experience with model evaluation methodologies (LLM-as-judge, RAGAS)
- Familiarity with Model Context Protocol (MCP)
- Background in telecom or enterprise SaaS environments
- Publications or open-source contributions in GenAI