לוח משרות דרושים AI Engineer בהרצליה

Build Modular AI Capabilities: Design and implement self-contained "AI Skills" that connect to the Platforms core data layers.
Ship End-to-End Autonomous Agents: Build agents that don't just "think," but act. You will deploy agents that handle the heavy lifting of the security lifecycle.

Bridge the Gap to Non-Technical Users: Create the abstractions that allow our customer success teams and client stakeholders to trigger or chain these AI workflows.

Own the AI Infrastructure & Evaluation: Maintain and contribute to the environment where these agents are born, ensuring they are grounded in truth.

Were expanding our Data & AI group and looking for a passionate, experienced Senior Data & AI Engineerto join our mission. This is a unique opportunity to work at the intersection of data engineering, LLMpowered systems, agentic workflows, and cybersecurity innovation

What youll work on
Data Infrastructure & EngineeringDesign, build, and scale production-grade data pipelines using Databricks, Spark, and modern cloud-nativetechnologies. Ensure high standards of data integrity, system performance, reliability, and scalability.
Core Backend & PlatformDesign and contribute to scalable backend services and platform capabilities using microservices andevent-driven architectures. Build reliable APIs, integrations, and asynchronous data flows that support highscale AI, data, and cybersecurity use cases.
LLM & Agentic SystemsDesign, prototype, and integrate LLM-powered systems, including Retrieval-Augmented Generationpipelines, agentic workflows, tool-using agents, multi-step reasoning flows, and AI-driven automation. Workwith technologies such as AWS Bedrock, OpenAI, Anthropic, LangGraph, vector databases, and modernorchestration frameworks.
AI-Assisted Engineering & Developer ProductivityExplore and apply advanced AI coding assistants and software-engineering agents, such as Codex andClaude Code, to improve development velocity, code quality, debugging, testing, and experimentation.Build proof-of-concepts and internal tools that help engineering and research teams work more effectivelywith AI-powered development workflows.
Intelligent Cybersecurity FeaturesCollaborate with Security Researchers, Engineers, and Product teams to identify opportunities forintelligent, data-driven features that deliver actionable cybersecurity insights to customers. Transformcomplex cybersecurity and platform data into reliable, explainable, and useful AI-powered capabilities.

In this role on our AI team, you'll build and scale core AI systems that power our products. You'll work across agentic document intelligence, autonomous agents, compliance AI, and ML-powered insights, prototyping quickly, building robust evaluations, and shipping production-grade AI with real business impact.

What You'll Do:
Build and ship AI/ML solutions using LLMs, agents, RAG, and document understanding models, alongside classic ML.
Prototype quickly, validate feasibility, and turn strong POCs into production systems.
Evaluate models and architectures, apply testing and guardrails to improve agent and service reliability.
Research and apply emerging techniques: multimodal/document AI, agentic frameworks, synthetic data generation, and new architectural approaches.
Work cross-functionally with product, R&D, and compliance teams to deliver end-to-end solutions.
Contribute to scalable, secure architecture and engineering best practices for AI delivery.

In this role on our AI team, you'll focus on agentic workflow automation. You'll design, build, and deploy reliable AI agents that automate complex HR, payroll, and payment workflows, handling decision-making, document intelligence, and complex tasks that typically require human judgment.

What You'll Do:
Design and build agent-based systems that automate document processing, business insights, and complex enterprise workflows.
Build observability, evaluation, and feedback loops for agent behavior to improve reliability, accuracy, and trust in production.
Own the technical architecture and engineering standards for agentic systems.
Build or manage data pipelines to process large volumes of documents and unstructured data.
Collaborate with domain experts to identify high-value automation targets and deliver end-to-end solutions.
Stay current on agent frameworks, LLM capabilities and limitations, and apply emerging patterns pragmatically in production.

We are looking for a Principal Software Engineer at the intersection of AI/ML and security. You will drive the architecture and delivery of autonomous agentic experiences that investigate, triage, remediate, and harden customer environments using LLMs, knowledge graphs, and the Security Graph (MSG).

This is a high-impact IC role reporting to the Exposure Management engineering leadership. You will influence strategy, mentor senior engineers, and ship production systems used by the largest enterprises in the world.

Responsibilities

Architect & Build Agentic Security Experiences
Design and implement Blue (investigate/triage), Green (remediate/harden), and Red (attack simulation) agents operating across customer environments.
Define agent architecture: planning, tool use (MCP skills), memory, context retrieval, and human-in-the-loop controls.
Build orchestration over the Security Graph, correlating exposure signals across cloud, device, identity, data, and AI.

Drive AI-Native Platform Design
Architect skill, knowledge, context, and memory layers for agentic security systems.
Design MCP-based skill interfaces for customization and extensibility.
Define how LLMs interact safely and accurately with structured security data (vulnerabilities, misconfigurations, attack paths, graph relationships).

Shape AI-for-Security Strategy
Partner with PMs and domain leads to define where AI autonomy vs. human involvement is needed.
Evaluate and integrate foundation models (Azure OpenAI, fine-tuned models) for tasks such as risk scoring, remediation planning, and blast radius analysis.
Stay ahead of industry solutions (Wiz AI-APP, CrowdStrike Charlotte AI, Palo Alto XSIAM).

Technical Leadership & Influence
Set technical direction across multiple teams (20-30 engineers).
Drive architecture decisions, design reviews, and engineering excellence.
Mentor senior engineers (L63-L65) and grow the AI-for-security discipline.
Represent the team across us (Security Copilot, MSG, Azure AI, Research).

A hands-on engineering role focused on building production-grade AI/ML systems and the automation infrastructure that supports them - driving AI adoption into developer workflows, internal tooling, and domain-specific applications across the organization.

As a member of the AI Infrastructure & Applications team, you will lead the design, development, and production deployment of AI/ML-powered systems alongside the automation infrastructure and developer platforms that support them.
You will architect intelligent, scalable solutions used across the organization - driving AI adoption into developer and automation workflows, internal tooling, and domain-specific applications - while also building and maintaining the automation frameworks and infrastructure those systems depend on.
The systems you build are expected to be production-grade, reliable, observable, and continuously improving.

Responsibilities
Architect and ship end-to-end AI-powered applications and pipelines, from prototype to production.
Build agentic systems, RAG pipelines, and tool-use patterns that integrate LLMs into real workflows.
Define and own AI quality metrics (accuracy, groundedness, hallucination rate, task completion) and integrate them into CI/CD release gates.
Design evaluation frameworks for non-deterministic systems: offline evals, human-in-the-loop review, and automated regression suites.
Harness AI/LLMs to extend and enhance existing automation infrastructure, improving system performance and operational efficiency.
Build scalable automation frameworks, APIs, and tooling used across the organization.
Collaborate with engineering, CI, and domain teams to address automation needs across hardware, software, and cloud.
Distill requirements from a large, diverse user base into generic, reusable, maintainable solutions.
Implement monitoring, drift detection, and structured feedback pipelines for continuous improvement.
Apply rigorous engineering discipline - test design, release criteria, rollback strategies - to AI-native deployments.
Partner with product, design, and domain experts to define use cases, acceptance criteria, and rollout plans.