דרושים » AI » AI Security - AI Platform Team Lead

This is your opportunity to get on the rocket ship and join a company that is building a cutting-edge enterprise network and secure cloud platform, and is on a fast track to becoming the worldwide market leader - dont miss it!
we are building a real-time AI runtime platform for security algorithms running inline across our global cloud and physical PoPs.
We are looking for an AI Platform Engineer to help build the infrastructure that powers high-throughput, low-latency AI security decisions in production.
You will work on a runtime engine that combines GPU-based models, from MMBERT-style models to LLMs, with CPU-based heuristics and security logic, optimized for scale, performance, reliability, and real-time execution. This is a versatile engineering role that spans AI runtime infrastructure, high-performance backend development, GPU inference, model lifecycle, and close collaboration with research teams to bring AI security algorithms into production.
Responsibilities
Build our companys AI security runtime platform for high-throughput, low-latency production serving.
Develop infrastructure for model serving, multi-model orchestration, and inline decision flows.
Optimize inference performance: batching, caching, streaming, GPU utilization, memory usage, and runtime acceleration.
Build backend orchestration and performance-critical services in Go.
Support the model lifecycle: registry integration, packaging, versioning, deployment, monitoring, and operational health.
Work closely with research and algorithm teams to productionize AI security models and algorithms at scale.

Required AI Platform Engineering Team Lead - Sovereign AI Engineering
We're building AI that nations own and control, deployed where almost no one else can operate. ingesting and structuring complex data, and driving practical actions that can literally impact the lives of billions of people around the world. This role helps make that real.
The Dream Job:
It starts with you - a technical leader driven to build both the agentic AI platform and the engineering team behind it. You care about backend quality, platform reliability, and growing engineers through real ownership. We are AI-first across the board - every team builds and operates agents. You'll set the technical direction for the platform that makes this possible: agent orchestration frameworks, LLM gateways, evaluation infrastructure, tool-calling systems, and retrieval pipelines. Without this platform, agents don't ship - you own the layer that turns AI research into Sovereign AI products, deployed across cloud and on-prem environments. You stay close enough to the codebase to debug production incidents, unblock your engineers, and make sound architecture calls.
If you want to make a meaningful impact, join our mission and lead the team that builds the agentic AI platform driving Sovereign AI products - this role is for you.
Responsibilities
Architect and evolve the AI platform - agent orchestration, LLM gateways, context engineering pipelines, evaluation infrastructure, tool-calling systems, and retrieval pipelines - through RFCs, prototypes, and design reviews.
Lead and grow a small team of AI Engineers building the agent framework, production backend services, and AI platform infrastructure - hire, mentor, pair on hard problems, and raise the bar through hands-on code and design reviews.
Contribute to critical systems, debug production incidents, and maintain enough codebase context to make sound technical calls.
Own reliability across AI and agent services - set and enforce SLAs, build observability for non-deterministic systems, and harden tool execution environments for cost and security.
Set the standard for AI engineering practices - agent testing strategies, evaluation frameworks with human-in-the-loop oversight, retrieval quality benchmarks, and CI/CD for AI systems.
Work closely with ML Platform, Data Platform, DevOps, Data Science, and Product teams across the Applied AI Engineering group - ensure the AI platform evolves to serve teams building agentic workflows across the organization.
Measure and improve developer experience - deploy friction, onboarding time, CI turnaround - as seriously as system performance.

We are seeking an Senior AI Engineer to design, build, and deploy AI-powered capabilities within our product.

This role focuses on integrating machine learning models and large language models (LLMs) into scalable software systems and delivering reliable AI-driven features to production.

The AI Engineer works at the intersection of software engineering, AI systems, and infrastructure. transforming AI technologies into practical applications.

Responsibilities:
Build applications powered by machine learning and large language models (LLMs).
Implement capabilities such as intelligent assistants, semantic search, automation, and recommendation systems.
Integrate AI functionality into backend services and product workflows.
Design and implement retrieval pipelines, embedding pipelines, and inference workflows.
Build Retrieval-Augmented Generation (RAG) systems and AI-driven services.
Create scalable AI architectures capable of handling production workloads.
Package and deploy AI models as production services.
Optimize inference performance, scalability, and latency.
Monitor AI services to ensure reliability and performance.
Develop backend services and APIs that expose AI capabilities.
Integrate AI systems with databases, internal services, and external APIs.
Contribute to system architecture and microservices design.
Implement logging, metrics, and observability for AI systems.
Track model performance and system reliability in production environments.
Work closely with product managers, engineers, and data scientists.

We are looking for a visionary AI Lead to build our internal AI platform and architect adaptive intelligence systems that serve as a dedicated security architect for each of our customers. You will lead a team of AI Researchers and Engineers to move beyond simple integrations and build true autonomous security solutions.
What You Will Build:
Autonomous Workflows: Build agents that execute security workflows end-to-end, automating detection, investigation, and response processes.
Runtime Defense: Productize research into active runtime guardrails and defensive control mechanisms.
Internal AI Platform: Build the shared infrastructure for models, training, and evaluations to enable safe, scalable AI features across the organization.
Adaptive Intelligence: Oversee the development of models that learn from customer-specific environments to generate high-signal, personalized insights.
Responsibilities:
Lead the AI Research Group, managing AI Researchers, Engineers, and DevOps.
Own the intelligence engine end-to-end, delivering next-generation analytics and contextual visualization features.
Drive the strategic "build vs. partner vs. buy" decisions for cloud prevention and AI controls.
Collaborate with the Threat Team to develop AI-driven threat detections against emerging attack vectors and novel AI threats.

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.

You will manage the AI Platform Engineer(s), set the technical standards for the AI Power User group's citizen development program, and serve as the connective tissue between business leadership, platform owners, and development teams. You will shape the multi-year AI architecture roadmap while also rolling up your sleeves to conduct architecture reviews, resolve blockers, and move use cases from concept to production. This is a role for someone who can think big and execute - and who understands that in an enterprise context, the quality of your governance is inseparable from the quality of your architecture.
What You'll Own
Strategy & Architecture
Define and own the enterprise AI integration strategy - identifying opportunities to embed intelligent automation, agentic workflows, predictive analytics, and generative AI capabilities across our core platforms
Develop and maintain reference architectures, design patterns, and the AI architecture decision log that governs how AI models connect to enterprise systems and what they are permitted to do
Consult on enterprise system architecture and implement best practices for the Enterprise Business Systems team to leverage in their day-to-day execution.
Lead Proof-of-Concept initiatives for new AI tools and platform-native AI features, evaluating them against build-vs-buy criteria before recommending adoption
Partner with business stakeholders to translate operational pain points into AI use cases with clear ROI framing and sequencing criteria
Contribute to our enterprise data strategy, ensuring AI initiatives are supported by clean, accessible, and well-governed data pipelines
Integration Architecture & Delivery

Design and own the Workato eMCP layer - the MCP governance model, persona-scoped token framework, workspace isolation strategy, and the single sanctioned action surface through which all AI agents write back to enterprise systems
Define integration patterns and standards for AI model connectivity (Claude, ChatGPT) to Salesforce, NetSuite, HiBob, and Jira - specifying what agents can read, what they can write, through which surfaces, and with what confirmation and audit requirements

Required Senior AI Platform Engineer - Sovereign AI Engineering
The Dream Job
It starts with you - an engineer driven to build the agentic AI platform that turns LLMs into reliable, production-grade capabilities. You care about clean APIs, well-defined service boundaries, and systems that teams can build on with confidence. We are AI-first across the board - every team builds and operates agents. You'll architect and ship the platform that makes this possible: agent orchestration frameworks, LLM gateways, evaluation pipelines, tool-calling infrastructure, and retrieval systems. Without this platform, agents don't ship - you own the layer that turns AI research into Sovereign AI products, deployed across cloud and on-prem environments.
If you want to make a meaningful impact, join our mission and build the agentic AI platform that drives Sovereign AI products - this role is for you.
Responsibilities
Design and build agentic systems - single and multi-agent workflows with planning, memory, context engineering, and tool use - for both internal automation and product-facing autonomous capabilities operating over long time horizons.
Build and operate the AI platform layer - LLM gateways, prompt management, structured output handling, tool-calling infrastructure, and cost/latency optimization - deployed on Kubernetes, consumed by every team for their agentic work.
Own the agent framework layer - orchestration primitives, execution environments, state management, and sandboxed tool execution - giving every team the building blocks to create and operate their own agents.
Build evaluation infrastructure that gives teams confidence in agent behavior - automated LLM and agent evals for quality, correctness, safety, latency, cost, and regressions, including human-in-the-loop oversight for mission-critical workflows.
Productionize and harden backend services (APIs, gRPC, async workers) that integrate LLMs - with proper error handling, retries, circuit breakers, and high-availability patterns.
Own RAG pipelines and retrieval systems - indexing, chunking, embedding, vector database management, filtering, and relevance tuning for production retrieval.
Optimize performance and cost across the AI stack - model routing, caching, batching, and inference cost management.
Ship shared tooling - libraries, SDKs, agent templates, and documentation - while working closely with ML Platform, Data Platform, DevOps, and other teams across the Applied AI Engineering group. Own architecture, documentation, and operations end-to-end.

we are building the future of AI Security - a platform that enables organizations to adopt AI boldly while keeping it governed, observable, and secure.
Were looking for a hands-on Software Engineering Team Lead to own our AI Security product for coding agents - from Claude Code, Codex, and GitHub Copilot to cloud-native coding environments and fully autonomous development agents.
This is a high-impact, product-driven leadership role at the intersection of AI, developer experience, cloud infrastructure, and enterprise security. Youll lead a full-stack team that owns this domain end-to-end: from infrastructure and policy engines to delightful product experiences that engineering teams actually love using.
What Youll Do
Lead and grow a full-stack engineering team dedicated to securing AI coding agents and autonomous development workflows.
Own the entire product area - strategy, architecture, roadmap execution, quality, customer adoption, and continuous iteration.
Design and build production-grade capabilities spanning AWS & Kubernetes infrastructure, backend services, real-time policy & enforcement engines, rich APIs/integrations, and intuitive UI/UX.
Partner closely with Product, Design, Sales Engineering, and customers to stay ahead of emerging AI risks and developer needs.
Set a high bar for engineering quality, ownership mindset, fast delivery, and product thinking across the team.
Help shape how the industry solves one of the biggest challenges of our time: letting AI agents move at incredible speed without compromising security or governance.

we are looking for a AI Architect.
Responsibilities:
1. AI Architecture & Technical Leadership
Guide AI architectural direction across Navinas 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 Director, AI & Machine Learning.
As the Director of AI & ML you will serve as the strategic and technical anchor for our Israel based AI and Machine Learning functions reporting directly to the VP of Data & AI. You will own the delivery of the core capabilities that power our product bridging the gap between high level business strategy and production grade AI execution. This is a high impact leadership role designed for a senior technical partner who can drive cross organizational alignment across Product, Engineering and Platform teams.
We are looking for a leader who moves beyond a traditional management layer to represent the DS/AI function in leadership discussions and strategic planning while maintaining a close operating rhythm with global leadership. In this role you will ensure our AI initiatives are integrated into the heart of the product lifecycle while serving as a credible senior partner for the technical leads. You will be responsible for shaping the future of how our organization builds and scales intelligent systems that handle sensitive healthcare data with precision and scalability.
Scope & Team
This role owns two teams within the Data & AI group:
AI Team: Builds patient-facing AI capabilities, NLP models, and clinical decision-support features.
ML Team: Owns production ML pipelines, model monitoring, and machine learning infrastructure.
Beyond direct team management, this role carries significant cross-organizational strategic responsibility:
Product interface: Bridge the gap between PMs and DS/AI team leads. The TLs are invested at the project level - this Director operates at the strategic level, ensuring AI/ML priorities are shaped early in the product lifecycle, not bolted on after. Be a force of change in how Product and DS/AI collaborate.
Engineering interface: Work peer-to-peer with engineering directors on shared execution dependencies, technical architecture decisions, and resource planning. Own this relationship at the Director level rather than leaving it to individual TLs.
Platform interface: Partner with Platform on infrastructure, data pipelines, and tooling strategy that underpins ML/AI delivery. Ensure DS/AI needs are represented in platform roadmap decisions.