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We are looking for an experienced Software Engineer (4+ years) to join the team as an AI Integration Engineer, supporting one of our enterprise clients as part of their AI-Native Development program.
In this role, you will act as an AI Champion and Technical Mentor within the clients R&D organization. Your mission is to help development teams effectively integrate AI tools, agentic coding workflows, and best practices into their daily software development lifecycle (SDLC). You will help guide teams through AI adoption, help overcome resistance, and ensure AI delivers real value.
This is not a feature-delivery or coding-focused role, especially in the early stages, the position is centered on enablement, training, and integration, working closely with teams to embed AI development tools into real workflows and support the organizations AI transformation.
This is a high-impact role focused on technical enablement, AI expertise, and effective collaboration with development teams, and intended for experienced engineers who actively use AI in their day-to-day development work and are looking to influence how teams work, rather than for junior profiles or engineers seeking a purely coding-focused position
What Youll Do:
Help drive adoption and optimization of Claude Code and/or similar Agentic coding tools
Support software and QA engineers, answer technical questions and help unblock teams
Stay on the cutting edge of AI releases and best practices, turn your learnings into shareable assets (custom rules, agents, skills, demos, playbooks, etc.)
Measure impact, identify gaps and improvement opportunities across the SDLC
Help defuse resistance to change with patience and empathy
Partner with DevOps, architects and management to track and optimize AI utilization.

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.

As a DevOps Engineer , you'll be a technical authority in everything cloud-native - leading feature development, making key architectural decisions, and collaborating with cross-functional teams to drive innovation.

Take an active role across all DevOps areas: infrastructure and cloud environment, tools, services, and uptime.
Contribute to the product's architectural and infrastructure design; evaluate and implement new cloud technologies and open-source tools to improve delivery and availability of the product.
Plan and drive the growth and scale of data capacity across products.
Own the smooth, production-grade execution of delivered solutions.

We're seeking a talented, experienced Frontend Engineer to join our Engineering team, tackling complex engineering challenges daily, improving our solutions, and driving innovation in Kubernetes optimization while working with cutting-edge technologies alongside major cloud providers such as AWS, GCP, and Azure.

Join a team of top-notch, skilled professionals building our core product offering
Build a world-class Kubernetes resource management platform that improves the productivity of DevOps teams across the world
Own the design and implementation of top-tier UI components, ensuring a smooth user experience
Enjoy a high degree of autonomy and responsibility
Make a high impact on the company's future, working on the core product while collaborating with top-tier engineers in a culture of innovation

We are building the autonomous infrastructure management platform that will power the next decade of enterprise compute.

What You Will Be Doing
Lead and grow a high-performing engineering organization responsible for our core product offering.
Define and execute the engineering strategy for our Kubernetes resource management platform, enabling DevOps teams worldwide to operate more efficiently.
Partner closely with Product, Design, and Go-to-Market leaders to translate business strategy into engineering execution.
Drive technical excellence, architectural direction, and engineering best practices across multiple teams.
Build, mentor, and develop engineering managers and senior engineers, fostering a culture of ownership, innovation, and continuous improvement.
Scale engineering processes, organizational structure, and delivery capabilities while maintaining a high bar for quality and execution.
Balance hands-on technical leadership with organizational leadership, making key architectural and strategic decisions when needed.
Create an environment where talented engineers can do their best work with a high degree of autonomy, accountability, and collaboration.

The role is simple to state and hard to do well: make that optimization save more tokens without ever costing output quality or breaking a provider's cache. It's a research-heavy engineering role - you form theories from real traffic and ship the ones that prove out on live customer requests.

Why this role
LLM optimization is a young field with no settled playbook, so the optimizations you invent here are genuinely new - and they ship into production, not a paper. And they matter: real enterprises run enormous volumes of LLM traffic and feel every wasted token, so the work you do lands on live customer requests and solves a problem they actually have. It's about as close to the frontier, and as close to the product, as engineering gets.

LLM optimization is a young field with no settled playbook, so the optimizations you invent here are genuinely new - and they ship into production, not a paper. And they matter: real enterprises run enormous volumes of LLM traffic and feel every wasted token, so the work you do lands on live customer requests and solves a problem they actually have. It's about as close to the frontier, and as close to the product, as engineering gets.

03What you'll do
Invent and ship optimizations. Design new strategies across every layer of a request - prompt and context compression, tool pruning, file and dev-command trimming, semantic caching, cache-prefix stabilization - and take them all the way to the hot path. Each one has to cut tokens on real traffic without degrading output.
Prove every win. Build the evals and the token/quality instrumentation that separate a genuine saving from a plausible-sounding one. An optimization only ships when the numbers back it.
Live in the caching trade-offs. Provider prompt caches only pay off on byte-stable prefixes, and a naive rewrite can cost more than it saves. Knowing exactly when an optimization actually wins - deterministic transforms, per-session decision pins, semantic dedup - is a lot of the job.
Build and own the gateway that runs them. The real-time proxy that carries production LLM traffic - streaming, retries, backpressure - executing every optimization on the live request path.
Keep it fast and deploy anywhere. Defend a tight latency budget on the hot path, and package the whole thing so it installs cleanly into any customer's cloud - AWS, GCP, Azure, containers, Kubernetes - and upgrades without drama.
Own systems end to end. From first commit to code serving live customer traffic - plus the spend-visibility and governance product built on top - with a direct line to the founders.

We're looking for a Senior Applied Researcher and Team Lead to help shape how our conversational agents are designed, evaluated, and shipped reliably at production scale.



Key Responsibilities

Partner directly with the Applied Research Team Lead to define and evolve team process, technical standards, and the quality bar for agent behavior.
Take real user experience and data through the full arc, from concept to a reliable agent design to a shipped, production-grade product.
Own or share ownership of the standing function of assessing AI tools, models, and providers for reliability and fit.
Raise the technical bar across a team spanning research-leaning grad students to engineers newer to AI, and turn personal expertise into shared tooling and standards the whole team can use.
Operate pragmatically inside current infrastructure constraints while flagging where structural fixes are needed.

We are seeking a visionary and highly influential Principal Software Engineer / Software Architect to define and drive the architectural direction of unified DataOps platform. This is a strategic and hands-on role where you will be responsible for the holistic design of our large-scale, cloud-native solutions, ensuring they are robust, scalable, secure, and maintainable. You will be instrumental in evolving our technology stack, ensuring our architecture supports future business growth and innovation, and mentoring the next generation of technical leaders.

What You'll Do
Define & Own Architecture: Define, evangelize, and own the overall software architecture vision and strategy for unified DataOps platform.

Lead System Design: Lead the design and evolution of complex, large-scale, and highly distributed cloud-native systems on AWS, ensuring high performance, scalability, reliability, and security.

Establish Standards: Establish and enforce architectural standards, best practices, and patterns across all engineering teams.

Strategic Collaboration: Collaborate closely with executive leadership, product management, and engineering teams to translate business strategy into actionable technical roadmaps and architectural solutions.

Hands-on Guidance: Provide hands-on architectural guidance, review critical designs, and contribute to proof-of-concepts or complex coding challenges when necessary.

Risk Mitigation: Proactively identify and mitigate architectural risks, anticipating future technical challenges and designing resilient solutions.

Technical Leadership & Mentorship: Mentor and provide strong technical leadership to senior engineers and other architects, fostering a culture of technical excellence and continuous learning.

Technology & Tooling: Drive technology selection, evaluation, and adoption processes, ensuring alignment with architectural principles and business needs.

Our Professional Services is seeking an Experienced system Engineer We are seeking an Experienced system Engineer to join our team and drive the development of diverse, cutting-edge UAV systems.
If you are looking for a meaningful technological journey filled with multidisciplinary challenges, come and be a part of our story. Key Responsibilities:
* End-to-end development of software- and hardware-intensive UAV systems, from solution definition through technical requirements authoring, integration, testing, and customer delivery.
* Leading complex integration processes, including the integration of products, payloads, and airborne subsystems.
* Defining system -level requirements for various components and authoring detailed specifications and technical documentation.
* Leading cross-functional development and integration efforts across diverse engineering teams, including Airborne Software, C2 (Command & Control) Software, Simulators, QA /Testing, Electronics, and more.

We are seeking an experienced Software Engineer to join our growing R&D team in Tel Aviv, reporting to the Engineering team manager. As part of this role you will, design, develop, and deliver high-quality, scalable, reliable, and extensible products. Work within our multidisciplinary R&D teams (Software Engineers, Linguists and NLP Research Engineers) and with the Product Management team.

This is an exceptionally exciting time to join a real AI company - when the very foundations of NLP are being redefined in front of our eyes. Youll be at the forefront of that transformation, helping to shape the next generation of AI systems that are not only cutting-edge but also deeply impactful.

As a Performance Software Engineer, you will be instrumental in building the core computation software that runs on custom AI accelerators. Your work will bridge the gap between host systems and proprietary hardware, encompassing everything from a custom on-board kernels to the sophisticated tooling required for real-time debugging. You will be a key player in chip bring-up and the long-term development of high-performance computation primitives and kernels, ensuring our architecture operates with maximum efficiency and deterministic performance.

Responsibilities
Develop and optimize low-level software primitives in C/C++, assembly, and architecture-specific intrinsics, primarily for RISC-V and ARM platforms.

Implement performance-critical kernels, runtime components, hardware abstraction layers, drivers, and firmware operating in bare-metal or highly constrained environments.

Work closely with hardware architects and RTL/design teams to define and validate hardware-software interfaces, memory maps, register interfaces, interrupts, synchronization mechanisms, and boot flows.

Analyze and optimize software for latency, throughput, memory footprint, instruction count, and efficient use of processor and accelerator features.

Bring up and debug software on simulation, emulation, FPGA, and silicon platforms, including low-level hardware/software integration issues.

Develop validation tests, benchmarks, and diagnostic tools for new processor, DSP, accelerator, and SoC functionality.

Contribute to architecture decisions around ISA extensions, compiler intrinsics, memory hierarchy, DMA, cache/coherency behavior, and software-visible hardware features.

As a Drivers & Virtualization Engineer, you will own the host-facing software boundary. Your mission is to ensure our hardware is accessible, secure, and performant across diverse environments-ranging from bare-metal deployments to complex virtualized and multi-device cloud infrastructures. You will focus on building robust kernel-level interfaces where correctness, isolation, and long-term operational safety are paramount.

Responsibilities
Develop and maintain high-performance Linux kernel drivers for proprietary accelerator.

Implement communication protocols and virtual device interfaces to bridge hardware and software.

Manage low-level hardware interactions including PCIe, DMA, MMIO, and interrupt handling.

Enable seamless virtualized access to accelerators using QEMU and KVM.

Design and implement robust device sharing, passthrough, and isolation mechanisms.

Ensure the security of accelerator operations through strict memory protection and resource isolation.

Debug complex failures that span the entire stack: from hardware and kernel to hypervisor and guest OS.

Support the scaling of our architecture from single-device setups to large-scale multi-device deployments.

We are looking for an AI-first Software Engineer who is highly product-oriented and excited about building customer-facing systems using modern AI tooling to dramatically increase development speed and operational automation.
What youll do?
Build and ship end-to-end product features (backend, frontend, integrations, automation).
Use AI tools and build the AI infrastructure to accelerate development through scalable workflows, including multi-agent approaches.
Build AI agents that fully automate customer-facing sales and service flows, in a business with a massive volume of textual interactions.

Joining us means becoming part of a company that solves real problems for real customers while giving you the opportunity to learn, grow, and make an impact.

What will your work be like?

Join a Zerto development team working in an Agile/Scrum environment.

Develop new features and solve challenging bugs.

Contribute to a distributed, multi-service system with complex business logic.

Work with a modern, continuously evolving technology stack, including Kubernetes, .NET Core, virtualization APIs, and more.

Use AI throughout the software development lifecycle-including research, design, coding, code reviews, environment provisioning, product deployment, and bug analysis-with tools such as GitHub Copilot, OpenCode, Atlassian Rovo, and MCP servers.