AI-Data Engineer

Location: Tel Aviv, Israel
Capacity: Fulltime

Company Overview: Tonic Security, the contextualized security company, is cybersecurity startup that is reshaping Exposure and Vulnerability Management by providing the context security teams need to accelerate prioritization and remediation of vulnerabilities and threats. Powered by an AI Data Fabric, Tonic extracts meaningful and actionable context from unstructured organizational knowledge and threat intelligence, empowering security teams with superior visibility, dramatic reduction in false positives, and remediation of findings that actually matter. Leading organizations, including Fortune 500 companies rely on Tonic to slash remediation time and reduce risk to key business processes.

Our team is composed of seasoned professionals, researchers, and industry veterans committed to redefining cybersecurity with agentic AI, and the latest cyber technologies.

Position Overview: As a AI-Data Engineer, you’ll take a leading role in building the foundation of Tonic’s Agentic Exposure Management platform: the source of truth that tells a security team what they have, what’s actually risky and why, how to fix it, and who is responsible.

Enterprises run their security across a sprawl of cloud and on-prem tools. Making sense of that sprawl is the heart of what we do. We’re building a complex data fabric that reconciles data from a hundred different sources, applies sophisticated transformation and enrichment, and turns partial, uncertain signal into one trustworthy model of an organization’s real exposure.

Key Responsibilities:

Model the security graph. Design an ontology that spans both the modern cloud and the classical on-prem world, abstract enough to absorb 100+ different integrations under one model, yet rich enough to carry everything needed for decision making. Decide what’s an entity, what’s an edge, and what’s a denormalized attribute, across millions of assets and finding rows.
Own asset identity engineering and reconciliation. Upstream identifiers are uncertain and volatile. Engineer deterministic identity resolution for assets and findings arriving from different vendors.
Build LLM-based and agentic pipelines for data classification, entity extraction, and enrichment, optimizing for cost, latency, and precision in production.
Build the integration factory. Stand up a scalable integration platform that drives down time-to-integration.
Build and maintain complex ETL pipelines handling millions of records: incremental sync, complex correlation, and data quality observability and monitoring.

Qualifications:

Must Have:

Proven experience of tackling highly complex systems.
8+ years of backend development or data engineering experience.
Critical thinking and sound judgment under ambiguity.
Hands-on experience building with LLMs in production.
Experience integrating messy, inconsistent third-party APIs.
Experience with complex workflow orchestration (Airflow or similar).
CI/CD fluency. Extreme ownership culture. We ship our own code.

Nice to Have:

Cybersecurity background.
Data Science / ML background.
Experience with LLM / ML Classifiers.

What Makes This Role Unique

Massive data variety. Each source system comes with its own schema, its own quirks, and its own idea of what an “asset” is. The interesting question isn’t ETL. It’s how we build a system that generalizes across all of them without an army of analysts.
Small team, high ownership. We have managers but the organization is very meritocratic. We ship functionality that is immediately valuable to our customers. The potential for your individual impact is huge.
Strong company. Work alongside a team with deep cybersecurity and data engineering experience.
LLMs are core infrastructure here, not a side experiment. We build real AI-powered pipelines that run in production every day.

Why Join Us?