דרושים » AI » Senior AI Infrastructure Engineer

We are looking for a Senior Front-End Infrastructure Engineer to help shape the foundation of our digital ecosystem across multiple brands and products. You will build the infrastructure, tooling, and engineering capabilities that enable teams to move faster, release with confidence, and scale effectively. Your work will directly impact developer experience, platform reliability, performance, experimentation capabilities, and the overall success of our digital brands.
This role is focused on solving complex engineering challenges with organization-wide impact. We are looking for a developer with deep technical expertise, a platform mindset, and a strong sense of ownership, someone who understands how engineering decisions drive business outcomes and enjoys building solutions that improve how teams build, test, release, and scale software across the organization. You will partner closely with Engineering, Product, QA, Data, and DevOps teams to help shape the future of our frontend ecosystem.
Roles & Responsibilities:
Build and evolve the frontend infrastructure, shared tooling, reusable components, and engineering foundations that support multiple brands and product teams
Improve frontend performance through modern web architecture patterns, including server-side rendering, caching strategies, and request middleware
Drive initiatives that improve Core Web Vitals, performance, reliability, and user experience at scale
Own and enhance frontend CI/CD pipelines, release processes, and production readiness practices to enable fast, safe, and reliable deployments
Establish monitoring, observability, and incident response practices that improve platform reliability and production visibility
Build solutions for experimentation, feature flagging, and controlled rollouts to improve reliability and support data-driven decision making
Lead cross-functional technical initiatives, partnering with Engineering, Product, QA, Data, and DevOps teams to solve complex platform challenges
Act as a technical owner for strategic frontend infrastructure initiatives, leveraging modern technologies and AI-powered development workflows to drive long-term platform improvements and organizational impact
Provide technical guidance through code reviews, engineering standards, and knowledge sharing across teams.

We're forming a new AI Group and looking for a Senior AI Engineer to help shape it from an early stage - a greenfield, long-term effort to evolve how decisions are made across the platform using AI-driven systems. You won't just integrate APIs or build demos; you'll build the AI brain that works alongside (and increasingly drives) our core automation engine, with real production impact from day one and room to grow into technical leadership as the group scales.

Agentic AI Architecture: Design and build autonomous AI agents that analyze infrastructure in real time and make intelligent decisions. Work with modern agentic frameworks (LangGraph, PydanticAI) and conversational AI to create multi-agent systems - including troubleshooting, optimization, FinOps, and how-to agents. Leverage core LLM capabilities (tool-use, memory, retrieval) to operate safely in production.
Platform Integration & Intelligent Decision Systems: Develop MCPs to expose capabilities to AI agents that reason over infrastructure environments, metrics, configurations, and cost signals. Build integrations with tools like Slack, Jira, and AI-powered IDEs (Cursor, Windsurf) to deliver context-aware insights, from "why is this pod not scheduling?" to "how can we reduce costs by 30% safely?"
AI Model Development & MLOps: Build and deploy machine learning models that learn from infrastructure patterns - detecting the right resource policies for workloads, predicting optimal scaling triggers, and recommending GPU configurations. Own the complete ML pipeline from training to production, ensuring models are reliable, monitored, and continuously improving.
R&D AI Tools Development & Adoption: Build and embed internal AI tools to accelerate engineering, development, research, and support.
AI Tools for Business Impact: Develop AI-powered tools that help Sales and Support teams demonstrate value instantly - agents that analyze customer infrastructure, generate cost optimization reports automatically, and turn technical data into clear business recommendations.
End-to-End Ownership: Own AI systems from concept to production, ensuring they're fast (sub-2-second responses), reliable, safe, and cost-effective. Build evaluation frameworks to measure quality, implement security controls, and balance performance tradeoffs in production.
Technical Leadership: Define AI architecture and best practices as a founding member of the AI team. Make key technical decisions - choosing frameworks, designing multi-agent systems, establishing data governance - and shape how evolves from AI-enhanced internal tools to customer-facing AI products.

We're looking for a Senior Software Engineer, Infrastructure & Automation to join and play a key role in building the engineering infrastructure behind our AI-powered offensive security platform.

You'll design and build the infrastructure required to automatically validate and improve our platform and agents, including AI-powered agents for testing, evaluating, and automation of our systems that ensure our platform infrastructure and AI agents perform is reliable, repeatable, and effective at scale. Working closely with our AI Research, Security Research, and Engineering teams, among other things, you'll develop the infrastructure that enables rapid experimentation, deterministic validation, and continuous evaluation of autonomous AI attackers.

If you're excited about building developer platforms, testing infrastructure, and complex distributed systems in a fast-moving AI startup, we'd love to meet you.

Responsibilites
Design and build the testing and automation infrastructure that powers Tenzais platform and autonomous security agents. Define and shape the platform architecture for that goal.
Build deterministic and indeterministic validation tools for platform behavior and repeatable, data-driven evaluation for non-deterministic agent behavior.
Help develop continuous evaluation pipelines that measure performance, compare models and releases, detect regressions, and surface actionable results.
Build Python services and automation that orchestrate distributed test runs and manage their environments, inputs, and results.
Create internal tools and agents and CI/CD integrations that improve experimentation speed, developer productivity, and release confidence.
Partner with AI Research and Security Research to define evaluation methodologies, success criteria, and coverage.
Own systems end to end - from architecture and implementation through production reliability and continued evolution.
Contribute to technical design, architecture discussions, and code reviews across the engineering organization.

We are looking for a Senior AI Engineer to design and build production-grade, LLM-powered systems. You'll work at the intersection of software engineering and applied AI - shipping agents, RAG pipelines, and tool-using systems that solve real problems at scale. This is a hands-on, high-ownership role for someone who thrives at the frontier of what's possible with modern LLMs and isn't afraid to write the glue, the infrastructure, and the prompts that make it all work.
This is a **cross-functional, company-wide role**. You won't be embedded in a single product team - instead, you'll partner with every department to identify high-leverage opportunities and build AI-powered tools and workflows that boost productivity and efficiency across the entire organization.
This is a great opportunity to be part of one of the fastest-growing infrastructure companies in history, an organization that is in the center of the hurricane being created by the revolution in artificial intelligence.
What You'll Do:
- Design, build, and operate LLM-powered applications, agents, and workflows end-to-end - from prototype to production.
- Architect retrieval, context engineering, and tool-use strategies that make models reliable, accurate, and cost-efficient.
- Integrate LLMs with internal services, third-party APIs, and data stores to automate complex business and engineering workflows.
- Build, evaluate, and continuously improve evaluation harnesses for non-deterministic systems.
- Collaborate closely with product, research, and platform teams to translate ambiguous problems into shipped capabilities.
- Stay ahead of the rapidly evolving LLM ecosystem (models, frameworks, agentic patterns) and bring the best ideas into our stack.

We are looking for a Senior AI Platform & Innovation Engineer to join the AI Engineering Group in Tel Aviv.
This role is focused on designing and building the foundational AI platform that powers next-generation AI capabilities across products and engineering teams. You will help shape our AI strategy by building reusable platform capabilities, evaluating emerging AI technologies, and developing production-grade AI systems that enable developers, products, and business teams across the company.
What Youll Do:
Design and build reusable AI platform capabilities used across multiple products.
Architect and implement production-grade agentic systems, including multi-agent workflows, tool orchestration, memory and planning capabilities.
Design and build internal AI platforms such as organizational knowledge (Brain), AI developer tools, autonomous engineering workflows, and shared AI services.
Research, evaluate and integrate emerging AI models, frameworks and developer tools into the platform.
Collaborate closely with Data Science teams to transform research into scalable production-ready AI capabilities.
Design end-to-end AI architectures, from data ingestion and retrieval to orchestration, model integration and production deployment.
Build and operate production AI services using modern cloud-native technologies.
Improve the reliability, scalability and observability of AI systems.
Define engineering standards, reusable patterns and best practices for AI development across engineering teams.
Lead technical proof-of-concepts and drive adoption of innovative AI technologies.
Mentor engineers and provide technical leadership on complex AI initiatives.

1. AI Architecture & Technical Leadership

Guide AI architectural direction across platform, focusing on system design, model lifecycle, and integration of AI components into product workflows.
Act as a senior technical reviewer and thought partner for complex or cross-team AI design decisions.
Provide technical oversight on areas such as exploring new models/technologies/opportunities for the company
Surface architectural risks, tradeoffs, and long-term implications, including cost, security and compliance aspects and clearly advise the VP of AI when certain technical directions should not be pursued.
This role influences judgment, clarity, and experience, not through blocking authority.

2. Hands-on Applied Innovation (Core Pillar | ~50%)

Spend at least 50% of time hands-on, building:
End-to-end AI prototypes
Technical demos and proofs of concept
Exploratory implementations of new AI capabilities
Drive applied innovation that:

De-risks new technologies
Demonstrates feasibility and impact
Informs product direction and business opportunities
Build fast, concrete examples that teams can learn from and extend.
Transition successful prototypes to team ownership for further development and scaling.
This role is expected to lead AI innovation by doing, while working closely with product, medical and engineering

3. Best Practices & Technical Enablement

Define and promote best practices for applied AI development, including:
Rapid prototyping and vibe coding.
Agent design, orchestration, and evaluation patterns
Experimentation, benchmarking, and validation workflows
Help teams align on shared technical patterns, tools, and standards.
Identify opportunities to consolidate duplicated efforts and improve cross-team coherence.
Lead technical deep dives, architecture discussions, and design reviews.
4. AI Compliance & Regulatory Enablement (Technical Scope)

Ensure Navinas AI development practices align with applicable AI regulations for a software product handling sensitive medical data.
Define and guide AI-specific compliance practices, including data usage, transparency, evaluation, and documentation expectations.
Support and contribute to AI-related compliance and regulatory documentation, in close collaboration with Legal, Security, and Medical Research teams.
Serve as a technical point of reference for AI compliance questions.

We are looking for a Platform Security Director to lead the engineering of our security infrastructure. Your mission is to build a Security Machine that is as sophisticated as our AI. You will move us beyond traditional "defense" into a "Secure-by-Default" reality, where our multi-cloud infrastructure (AWS and Azure) is hardened, automated, and resilient by design.
Youll Own:
The end-to-end security strategy across Application Security, Cloud (AWS/Azure), and Platform layers, ensuring a Secure-by-Default framework, as measured by deployment coverage, critical vulnerability reduction, and developer adoption metrics.
A unified Security Engineering organization covering AppSec, SecDevOps, and AI Security leadership, collaboration points with adjacent teams (Infrastructure, R&D, Product Engineering), and building and mentoring a high-performing team.
Lead our Platform Security domain - from Layer 7 protections to AWS architecture, security tooling, automated risk scoring, and AI-assisted automation workflows that improve detection and remediation times.
Security controls (WAF, Rate Limiting) embedded into the SDLC and CI/CD pipelines, including mandatory security gates, artifact scanning, and automated deployment checks.
Lead our AI strategy - enabling secure use in our platform products as well as for our internal engineering usage, and scaling AI security governance and controls.
The evolution of scalable infrastructure security, including Kubernetes, containers, IAM, and Zero Trust architectures, strengthening Kubernetes and cloud security posture.
Own and execute the comprehensive Vulnerability Management program, from discovery (SAST/DAST/OSS scanning) through remediation tracking, and manage the external Bug Bounty program, with a focus on improving remediation SLAs and automation coverage.
Mature secure-by-default engineering practices across the SDLC.
Increase developer adoption of automated security tooling.
Youll Solve:
Eliminating manual bottlenecks through code-driven guardrails and frictionless developer experiences.
Securing complex production systems and multi-tenant AI architectures in cloud-native production environments, with a focus on model integrity, training data provenance, data exfiltration prevention, and minimizing unique risks associated with Large Language Models (LLMs), by applying specific AI threat models.
Operationalizing Red/Purple Team exercises, penetration testing, and the Bug Bounty program to ensure a continuous feedback loop that drives proactive risk reduction.
Youll Impact:
Support a culture where security is embedded into the foundation of engineering
Turn security into a strategic product advantage and customer trust differentiator
Improve developer experience by making the secure path the easiest path
Increase engineering velocity while strengthening platform resilience
Shape the future intersection of Security, AI, and Platform Engineering
Create scalable systems, processes, and relationships that grow with the companys innovation pace.

Join our AI Platform group as an AI Engineer in the AI Core team and help build the next generation of AI-powered networking and security solutions at our company.
The AI Core team is responsible for developing AI applications, intelligent agents, and backend systems that integrate directly into our company products and workflows. As part of our companys strategic AI direction, the team focuses on turning modern GenAI capabilities into scalable production systems that help customers operate, troubleshoot, investigate, and manage complex networking and cybersecurity environments more effectively.
This role combines strong software engineering, modern GenAI technologies, and deep product impact. Youll work on building production-grade AI systems, integrating LLMs and agentic workflows into large-scale products, and helping shape how AI transforms cybersecurity and networking operations across the company.
We are looking for engineers who are excited about building real-world AI systems in production, not just experimenting with models, but designing scalable applications, integrating intelligent agents into complex products, and solving challenging engineering problems with direct customer impact.
Key Responsibilities
Design, develop, and maintain AI-powered applications, agents, and backend systems
Build intelligent workflows and agentic systems integrated into our company networking and security products
Develop scalable APIs, services, and infrastructure supporting GenAI applications in production
Integrate LLMs, RAG pipelines, vector databases, and orchestration frameworks into robust production systems
Collaborate closely with AI researchers, software engineers, infrastructure teams, and product groups to deliver end-to-end AI solutions
Improve scalability, reliability, observability, and operational excellence of AI systems
Explore and adopt modern AI engineering methodologies, frameworks, and tooling
Participate in architectural and technical decisions shaping the future of AI across our company products.

We are hiring an AI Researcher to join the research team building the next generation of AI-native security systems. You will work alongside security and threat researchers to build large-scale AI agents that reason over software, code, endpoint activity, and security signals to detect malicious behavior, uncover vulnerabilities, assess risk, and make autonomous security decisions in real-world production environments. We are entering the Mythos era - where attackers operate at machine speed using autonomous systems and AI-generated software, and defenders must evolve the same way. We use state-of-the-art frontier models, including access to Mythos, to build reliable AI-native security systems at global scale. You will help design the evaluations, harnesses, and reliability infrastructure that make autonomous agents dependable under real customer load, while collaborating with leading AI organizations including Anthropic on initiatives such as Glasswing. This is an opportunity to work at the frontier of AI, autonomous systems, and cybersecurity while helping define how the next generation of security systems will operate.
Key Responsibilities
Build AI agents and autonomous security systems that reason over software, code, endpoint activity, MCPs, and security signals to detect malicious behavior, uncover vulnerabilities, and assess risk at production scale.
Develop systems, tooling, and infrastructure that enable agents to autonomously investigate threats, hunt for malware in massive datasets, and operate reliably in complex security environments.
Design and run experiments to evaluate frontier-model and agent capabilities in realistic adversarial scenarios, including benchmark creation, large-scale datasets, automated evaluations, and human-in-the-loop review systems.
Build the evaluation harnesses, observability systems, and reliability infrastructure required to make autonomous agents accurate, scalable, and dependable under real customer load.
Engineer for scale and performance across large distributed AI systems, including inference optimization, orchestration, batching, caching, cost controls, and graceful degradation under high demand.
Continuously evaluate emerging models, agent architectures, prompting techniques, and research directions to ensure our systems remain at the frontier of AI-native cybersecurity.
Rapidly prototype and test new approaches across reasoning, autonomy, evaluations, and security workflows as the AI landscape evolves.
Partner closely with threat and security researchers to extract domain expertise, translate analyst reasoning into AI workflows, and enable new forms of automation and autonomous investigation.
Collaborate with leading AI and security researchers to shape the future of AI-native cybersecurity as the industry transitions into the Mythos era.
Senior candidates will help define research direction, shape technical strategy, identify high-leverage problems, and influence how autonomous AI systems are deployed across the organization.

As the Head of AI Security, you will be the ultimate authority and strategic leader responsible for securing our AI/ML infrastructure, data pipelines, and deployed models, you will bridge the gap between advanced data science and enterprise-grade security. You will design, implement, and oversee security frameworks that protect our proprietary AI technologies, multi-agent systems, and RAG architectures.
Youll Own:
our companys end-to-end AI security strategy across AI/ML infrastructure, data pipelines, LLMs, RAG systems, and autonomous AI agents
Secure-by-design frameworks and security architecture for AI-powered products and platforms
AI threat modeling, adversarial testing, red teaming, and vulnerability management programs
AI governance, internal security policies, and alignment with emerging global AI regulations and standards
Security tooling, automation frameworks, and continuous monitoring capabilities for AI environments
Cross-functional security leadership across Data Science, AI Engineering, Product, Infrastructure, and SecOps teams
Youll Solve:
Protecting AI systems against emerging threats such as prompt injection, model inversion, data poisoning, and training data leakage
Preventing sensitive customer and enterprise data from leaking into training sets or model outputs
Securing complex AI architectures including LLMs, RAG pipelines, and multi-agent systems at scale
Embedding security seamlessly into the ML lifecycle without slowing innovation
Building scalable AI security operations through automation, monitoring, and continuous adversarial testing
Navigating evolving AI governance, compliance, and regulatory requirements across global markets
Youll Impact:
Maintain our companys reputation as a leader in secure and trustworthy enterprise AI
Build the foundation that allows AI innovation to scale safely and responsibly
Strengthen customer trust by protecting highly sensitive conversational and business data
Create resilient AI systems that can withstand real-world attacks and emerging threat vectors
Drive a security-first AI engineering culture across the organization
Shape the future of AI security, governance, and MLSecOps in a hypergrowth AI company.