DevJobs

Senior AI Engineer

Overview
Skills
  • Python Python
  • PyTorch PyTorch
  • Kafka Kafka
  • PostgreSQL PostgreSQL
  • Elasticsearch Elasticsearch
  • JIRA JIRA
  • CI/CD CI/CD
  • GitHub GitHub
  • AWS AWS
  • Azure Azure
  • GCP GCP
  • Kubernetes Kubernetes
  • Docker Docker
  • pgvector
  • Pinecone
  • Weaviate
  • Anthropic APIs
  • LangSmith
  • OpenAI
  • Confluence
  • LangChain
  • LangGraph
  • MLOps
  • AWS Bedrock AgentCore
Description

Tel Aviv

  • Hybrid | Full-Time | Cybersecurity

About DriveNets

DriveNets is redefining networking with a cloud-native, disaggregated approach that powers the world's largest service providers and hyperscalers. Our AI-era infrastructure enables carriers and enterprises to scale GPU clusters and AI workloads across distributed networks — faster, more efficiently, and at unprecedented scale.

The Role

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 DriveNets' 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

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

Why Join Us

  • Work on AI-powered cybersecurity for the networks that carry global internet traffic
  • Build and own production multi-agent systems — real scale, real impact
  • Greenfield AI team within Cybersecurity — you will shape how AI is built and deployed at DriveNets
  • Competitive salary, equity, and benefits
  • Flexible hybrid model from our Tel Aviv office
  • A culture of ownership, speed, and engineering excellence
DriveNets