דרושים » מחשבים ורשתות » Senior Principal DevOps Engineer (Cortex Cloud)

Required Senior Principal DevOps Engineer (Cortex Cloud)
Your Impact:
As a Senior Principal DevOps Engineer, you will serve as a visionary technical leader within the Cortex Cloud DevOps group. You will define the technical strategy and architecture that ensures our massive-scale production services remain highly reliable, exceptionally secure, and performant. You will pioneer the integration of AI-driven capabilities into our daily operations, establishing elite engineering standards and fundamentally transforming the workflows of hundreds of developers through autonomous agents and intelligent procedures.
Your Career:
Architectural Vision & Scalability: Design and scale massive, resilient distributed systems and global Kubernetes infrastructure, implementing robust observability and monitoring frameworks.
AI-Driven Transformation: Revolutionize the SDLC by integrating Generative AI, autonomous agents, and LLM-powered workflows into CI/CD and self-healing systems to accelerate developer velocity.
Technical Leadership & IaC: Define architectural standards, lead GitOps/IaC (FluxCD/Terraform) strategies, and mentor Senior/Staff engineers across the R&D organization.
Developer Experience & Efficiency: Build and champion AI-powered platforms that automate troubleshooting and eliminate friction. Partner directly with Engineering Directors, Principal Architects, and Product Management to align infrastructure initiatives with business goals, optimizing for scale, high availability, and multi-million-dollar cost-efficiencies.
Security & Compliance: Embed "Security by Design" principles into the platform architecture to ensure platform integrity without sacrificing delivery speed.

We are looking for an experienced Technical Lead to direct the technical vision across our Infrastructure Group (DevOps, Data Infrastructure, and Backend Platform).This person is deeply experienced in the DevOps and Data Infrastructure worlds, with solid working knowledge of the backend (Java) stack - enough to challenge service architecture and align infrastructure decisions with how services are actually built and run. This is a cross-cutting, technical role. The Technical Lead drives architecture, eliminates silos between teams, standardizes foundational engineering practices, and mentors senior engineers across the group

Team tasks you will take part in
Act as the infrastructure technical lead within the R&D organization, directly influencing our broader technology roadmap.
Drive the end-to-end technical strategy for cloud infrastructure across AWS and multi-cloud environments, from conceptual architecture to production readiness.
Collaborate with technical leads across engineering to ensure cross-group implementations align with our overall technical strategy.
Design and execute the rollout of AI infrastructure, including governed model access, LLM proxy architectures, inference/serving platforms, and MCP and agent infrastructure.
Establish AI as a first-class engineering capability across us (AI SDLC) by deploying governed coding agents, IDE assistants, AI-assisted code review, testing, and documentation-enforcing guardrails, cost controls, and usage visibility to deliver a measurable shift in throughput.
Architect and continuously evolve CI/CD systems and delivery pipelines at scale to reduce friction and elevate developer safety standards.
Collaborate directly with backend teams to architect scalable microservices and optimize datastore performance across RDS MySQL, MongoDB, and Snowflake.
Define and enforce organization-wide observability, reliability, and performance standards, establishing best practices for monitoring, alerting, and system health.
Cloud security, and incident management processes, ensuring systems remain resilient, secure, and compliant.

We are looking for an exceptional Senior / Principal Backend Engineer to join our AppSec Platform group, reporting directly to the Group Leader.
This is a highly autonomous, hands-on role for an engineer who wants broad technical ownership rather than a narrowly defined domain. You will work on some of AppSec's most important and challenging engineering problems - spanning high-scale distributed systems, data infrastructure, onboarding and integrations, developer experience, reliability, performance, and AI-driven engineering.
You will frequently operate across team boundaries, take ambiguous problems from idea to production, and lead technical initiatives that have an impact well beyond a single service or team.
We are looking for someone who combines deep backend expertise with strong execution: an engineer who can design the architecture, dive into the code, debug production systems, challenge existing assumptions, rapidly prototype new approaches, and bring others along with them.
Key Responsibilities:
Lead Critical Initiatives End-to-End - Own complex, business-critical engineering efforts from problem definition and architecture through implementation and production, often spanning multiple teams and domains.
Build and Scale the Backbone of AppSec - Design and evolve distributed systems and data infrastructure supporting large enterprise environments and hundreds of millions of entities, while balancing scalability, reliability, performance, and cost.
Stay Hands-On and Raise the Engineering Bar - Write and review production code, solve complex technical problems, and champion excellent architecture, maintainable code, testing, observability, and production ownership.
Drive Performance & Reliability - Identify architectural and operational bottlenecks and lead meaningful improvements in scalability, resiliency, efficiency, observability, and production readiness.
Shape DevEx and AI-Driven Engineering - Improve how engineers build, test, debug, and operate software while leveraging AI, automation, and agentic approaches to accelerate development and unlock new capabilities.

We are looking for a hands-on DevOps Engineer with a strong cloud-native mindset to build, maintain, and evolve our highly scalable, highly-available cloud infrastructure. This role is pivotal in driving operational excellence, security, and automation across our entire engineering organization. You will promote communication, integration, and collaboration to significantly enhance our software development productivity and reliability. You'll work closely with engineering and product teams to streamline delivery, enforce platform standards, and enable a high-velocity development environment-all while keeping reliability and security top of mind.
Responsibilities:
Design, Automate, and Manage complex cloud infrastructure on AWS using best-in-class Infrastructure as Code (IaC) practices.
Lead the operation and enhancement of our production Kubernetes environments (EKS), focusing on automation, security, observability, and seamless CI/CD integration.
Drive continuous improvement across platform tooling, developer experience, and operational processes to meet our ambitious performance and uptime goals.
Implement and enforce security-first infrastructure patterns, including strong IAM, network segmentation, and secure secrets management.
Actively contribute to high-level technical design discussions and cross-functional architectural decision-making, ensuring solutions align with long-term platform strategy.
Drive AI-Powered Automation: Design, build, and deploy infrastructure for autonomous AI agents and agentic flows, integrating AI tooling directly into developer platforms and CI/CD pipelines.
Architect AI Platform Infrastructure: Establish reliable, scalable, and secure AI/DevOps orchestration frameworks to enable continuous deployment and runtime management of AI-driven tools.

We are looking for an exceptional Senior / Principal Backend Engineer to join our AppSec Platform group, reporting directly to the Group Leader.
This is a highly autonomous, hands-on role for an engineer who wants broad technical ownership rather than a narrowly defined domain. You will work on some of AppSec's most important and challenging engineering problems - spanning high-scale distributed systems, data infrastructure, onboarding and integrations, developer experience, reliability, performance, and AI-driven engineering.
You will frequently operate across team boundaries, take ambiguous problems from idea to production, and lead technical initiatives that have an impact well beyond a single service or team.
We are looking for someone who combines deep backend expertise with strong execution: an engineer who can design the architecture, dive into the code, debug production systems, challenge existing assumptions, rapidly prototype new approaches, and bring others along with them.
Key Responsibilities:
Lead Critical Initiatives End-to-End - Own complex, business-critical engineering efforts from problem definition and architecture through implementation and production, often spanning multiple teams and domains.
Build and Scale the Backbone of AppSec - Design and evolve distributed systems and data infrastructure supporting large enterprise environments and hundreds of millions of entities, while balancing scalability, reliability, performance, and cost.
Stay Hands-On and Raise the Engineering Bar - Write and review production code, solve complex technical problems, and champion excellent architecture, maintainable code, testing, observability, and production ownership.
Drive Performance & Reliability - Identify architectural and operational bottlenecks and lead meaningful improvements in scalability, resiliency, efficiency, observability, and production readiness.
Shape DevEx and AI-Driven Engineering - Improve how engineers build, test, debug, and operate software while leveraging AI, automation, and agentic approaches to accelerate development and unlock new capabilities.

We are looking for a Principal MLOps Engineer with a deep focus on ML Platforms and Infrastructure to join our Data & AI group at Cortex Research. Our team is responsible for designing, building, and scaling the foundational MLOps and LLMOps platforms that power both our Data Scientists and Security Researchers. You will architect the high-performance core infrastructure that enables these roles to build, train, and deploy advanced AI systems-ranging from optimized Small Language Models (SLMs) to complex agentic workflows and RAG systems. If you are passionate about building scalable compute platforms and automating the full ML lifecycle to solve complex data and security challenges, we want to hear from you.
Key Responsibilities
Scale Distributed Training: Design and optimize infrastructure for training and fine-tuning LLMs and SLMs, leveraging distributed GPU workloads, efficient clustering, and compute optimization.
Automate the ML Lifecycle: Architect robust, automated pipelines for continuous training (CT) and deployment (CD) of models, ensuring a seamless flow from raw data collection to production environments.
Build Model Infrastructure: Own the serving architecture for LLMs/SLMs, balancing latency, throughput, and GPU utilization under production traffic.
Implement Advanced Monitoring: Establish comprehensive observability systems to monitor live model performance, data drift, and computational metrics, feeding insights back into the automated training loops for continuous improvement.
Collaborative Architecture: Partner closely with data scientists and security researchers to productize complex model architectures and streamline their workflows, while collaborating with our DevOps team to integrate with core cloud infrastructure.

As a Tech Lead DevOps Engineer, you'll own the technical strategy for our cloud infrastructure end-to-end, from architecture to production. You'll build the automation frameworks, IaC tooling, and CI/CD systems that let engineering ship fast and safely at scale, while setting the bar for observability, reliability, and incident response across the org. You'll work closely with engineering leadership to shape the infrastructure roadmap and make the tradeoffs between velocity, cost, security, and reliability, acting as a technical multiplier for the broader team.

What Youll Do
Responsibilities:
Own and drive the end-to-end technical strategy for cloud infrastructure across AWS and multi-cloud environments, from conceptual architecture to production readiness.
Design and implement advanced automation frameworks, internal developer platforms, and tooling utilizing Python, Bash, and modern infrastructure-as-code technologies (e.g., Terraform, CDKTF).
Architect and continuously evolve CI/CD systems and delivery pipelines at scale, reducing friction and elevating developer productivity and deployment safety standards.
Define and enforce organization-wide observability, reliability, and performance standards, establishing best practices for monitoring, alerting, incident response, and system health.
Partner closely with engineering leadership and cross-functional teams to shape infrastructure roadmaps, enable scalable deployments, and ensure resilient production systems.
Make and own high-impact technical tradeoff decisions that judiciously balance velocity, cost efficiency, security, and reliability in a dynamic, growth-oriented environment.
Serve as a technical mentor and force multiplier, elevating the infrastructure capabilities of the broader engineering organization and championing a DevOps-first culture.

We are looking for a Principal MLOps Engineer with a deep focus on ML Platforms and Infrastructure to join our Data & AI group at Cortex Research. Our team is responsible for designing, building, and scaling the foundational MLOps and LLMOps platforms that power both our Data Scientists and Security Researchers. You will architect the high-performance core infrastructure that enables these roles to build, train, and deploy advanced AI systems-ranging from optimized Small Language Models (SLMs) to complex agentic workflows and RAG systems. If you are passionate about building scalable compute platforms and automating the full ML lifecycle to solve complex data and security challenges, we want to hear from you.
Key Responsibilities
Scale Distributed Training: Design and optimize infrastructure for training and fine-tuning LLMs and SLMs, leveraging distributed GPU workloads, efficient clustering, and compute optimization.
Automate the ML Lifecycle: Architect robust, automated pipelines for continuous training (CT) and deployment (CD) of models, ensuring a seamless flow from raw data collection to production environments.
Build Model Infrastructure: Own the serving architecture for LLMs/SLMs, balancing latency, throughput, and GPU utilization under production traffic.
Implement Advanced Monitoring: Establish comprehensive observability systems to monitor live model performance, data drift, and computational metrics, feeding insights back into the automated training loops for continuous improvement.
Collaborative Architecture: Partner closely with data scientists and security researchers to productize complex model architectures and streamline their workflows, while collaborating with our DevOps team to integrate with core cloud infrastructure.

We are looking for a Principal MLOps Engineer with a deep focus on ML Platforms and Infrastructure to join our Data & AI group at Cortex Research. Our team is responsible for designing, building, and scaling the foundational MLOps and LLMOps platforms that power both our Data Scientists and Security Researchers. You will architect the high-performance core infrastructure that enables these roles to build, train, and deploy advanced AI systems-ranging from optimized Small Language Models (SLMs) to complex agentic workflows and RAG systems. If you are passionate about building scalable compute platforms and automating the full ML lifecycle to solve complex data and security challenges, we want to hear from you.
Key Responsibilities
Scale Distributed Training: Design and optimize infrastructure for training and fine-tuning LLMs and SLMs, leveraging distributed GPU workloads, efficient clustering, and compute optimization.
Automate the ML Lifecycle: Architect robust, automated pipelines for continuous training (CT) and deployment (CD) of models, ensuring a seamless flow from raw data collection to production environments.
Build Model Infrastructure: Own the serving architecture for LLMs/SLMs, balancing latency, throughput, and GPU utilization under production traffic.
Implement Advanced Monitoring: Establish comprehensive observability systems to monitor live model performance, data drift, and computational metrics, feeding insights back into the automated training loops for continuous improvement.
Collaborative Architecture: Partner closely with data scientists and security researchers to productize complex model architectures and streamline their workflows, while collaborating with our DevOps team to integrate with core cloud infrastructure.

Required Principal / Sr. Principal Security Research - Advanced Cyber Research (Cortex)
We are seeking a visionary and highly technical Principal / Sr. Principal Security Researcher to join our security research team. In this role, you will be at the forefront of innovating automated defense systems to rapidly protect platform customers against advanced cyber threats at an unprecedented scale. This position offers a unique opportunity to blend deep traditional cybersecurity expertise - including OS internals, reverse engineering, and advanced detection engineering with cutting-edge AI technologies like LLMs, SLMs, and agentic workflows.
You will spearhead the creation of innovative prevention solutions, leveraging massive streams of agent telemetry to design AI-driven logic that neutralizes threats before they execute. As a key technical leader, you will partner closely with top-tier data scientists and product engineers to translate cutting-edge security research into production-grade capabilities, ensuring we stay ahead of the evolving threat landscape.
Key Responsibilities:
Develop Automated Defense Systems:Create, engineer, and deploy automated solutions for the prevention and detection of advanced cyber threats, analyzing complex data to rapidly protect platform customers at scale.
Innovate with AI:Partner closely with data scientists to leverage LLMs, SLMs & deeplearning techniques to fuel these advanced prevention solutions and automated defense capabilities.
Research, develop, and continuously improve the Agentix solution. Drive the development of next-generation security content by designing and implementing specialized AI agents to automate security operations and workflows.
Drive Security Innovations:Spearhead new research initiatives from the ground up. Act as the primary driver across key stakeholders, leading top-tier data scientists and engineers to ensure the successful delivery of enterprise-grade security capabilities.