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'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 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.

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 company 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
Design and oversee API strategies, event-driven architectures, and middleware patterns that support scalable AI feature delivery - including agentic workflows, intelligent data transformation, anomaly detection, and natural language interfaces layered onto ERP and CRM data
Collaborate with Engineering during build phases, conducting architecture reviews, providing hands-on guidance, and resolving complex technical blockers
Define non-functional requirements - latency, security, auditability, model drift monitoring - for AI components embedded in mission-critical business processes
Establish MLOps and LLMOps practices appropriate for our enterprise environment: model versioning, observability, and rollback procedures for production AI workloads

Were launching a new Financial Infrastructure team at our company to build the next generation of our payments and platform capabilities from the ground up. This is a founding engineering role in our Tel Aviv office, with significant ownership, autonomy, and technical influence from day one.
Youll design and build foundational systems that power our companys platform, working on high-impact, mission-critical services and helping set the technical bar for the team.
What youll do:
Own features end-to-end - Take ideas from early concept through production. You wont just write code - youll ship real systems, iterate quickly, and see the impact of your work in production.
Think beyond the task - Take initiative, understand the broader system and business context, and help drive both product and technical decisions. This is not a ticket-driven role.
Leverage AI-accelerated development practices - Use modern AI coding tools to improve development velocity, code quality, and testing efficiency while building robust, secure, and scalable infrastructure.
Backend-focused, full-stack mindset - Your core focus will be backend systems, but youre comfortable operating across APIs, data, infrastructure, and DevOps when needed.
Design for scale - Build backend services that are reliable, maintainable, and architected to scale with the platform.

Were launching a new Data & AI team at our company to build AI-first products and foundational platform capabilities from the ground up. This is a founding engineering role in our Tel Aviv office, with ownership and influence from day one.
Youll design and build foundational systems that power the next generation of our companys platform, working on high-impact, customer-facing products and helping set the technical bar for the team.
What youll do:
Own features end to end - take ideas from early concept through production. You wont just write code, youll ship real products, iterate quickly, and see the impact of your work with customers.
Think beyond the task -Take initiative, understand the broader context, and help drive product and technical decisions. This is not a ticket-driven role.
Build AI-powered systems- Use AI where it meaningfully improves product quality, development velocity, or user experience, both in what we ship and how we build.
Backend-focused, full-stack mindset- Your core focus will be backend systems, but youre comfortable working across APIs, data, infrastructure, and DevOps when needed.
Design for scale - Build backend services that are reliable, maintainable, and designed to grow with the platform.

Were looking for a senior engineer who is ready to move beyond traditional software development and take full ownership of product delivery.

This is not a classic backend engineering role. You will work directly with Product to turn ideas and requirements into production-ready solutions, make the technical decisions, and use AI coding agents as a core part of your engineering workflow.

You will own the outcome, not just the code. AI-generated code is your code, and you are accountable for its quality, security, performance, and maintainability.


Own product features end-to-end, from requirements through production and monitoring.
Work with Product to translate business problems into clear technical solutions and execution plans.
Design scalable and maintainable solutions, including architecture, APIs, data models, and integrations.
Use AI coding agents to accelerate implementation, testing, debugging, and automation.
Review and improve AI-generated code, ensuring high standards for correctness, security, performance, and maintainability.
Make independent technical decisions and communicate architectural trade-offs clearly.
Contribute across backend, frontend, QA, CI/CD, and infrastructure when needed to deliver a feature.
Investigate and solve complex production and performance issues.
Continuously improve how we build, test, and ship software using AI-native development practices.

We are looking for a Senior II Software Engineer to join the CX Platform team, the foundational engineering team powering our entire Customer Experience group. This is a high-impact, hands-on role at the intersection of backend engineering, AI infrastructure, and customer-facing product.

You'll work across the full platform stack (backend services, data pipelines, security, cost, and scale) with a meaningful and growing focus on AI infrastructure. We own the agentic platform for the entire Product Offering group: from building the LLM infrastructure and agentic workflows to ensuring they're reliable, observable, and safe in production.

What you'll be doing:
Own AI infrastructure for the Product Offering group. Design, build, and evolve the shared AI platform (agentic workflows, LLM integrations, observability, and guardrails) that CX product teams build on.
Ship agentic features end to end. Lead development of AI-driven capabilities using LangChain, LangFuse, and AWS Bedrock, from architecture through production deployment and monitoring.
Drive platform architecture. Set the technical direction for the CX backend (services, data pipelines, API patterns) with an eye for scalability, reliability, and developer experience.
Own core data foundations. Design resilient data-access patterns across Snowflake, Elasticsearch, Kafka, Redis, and MySQL; keep pipelines fast, fresh, and reliable.
Mentor and elevate. Help engineers across the CX group grow in backend craft, AI engineering, and system design thinking.
Collaborate cross-functionally. Work with product, design, and customer-facing teams to turn ambiguous problems into well-scoped, high-quality solutions.

As a Software Solution Architect, you will lead the transformation of AI infrastructure. Our NVIS team focuses on developing the next generation of NVIS Central, an agentic software platform with tools, services, and AI agents that automate, simplify, and speed up the work of our delivery organization. This role offers an outstanding chance to create and build LLM-powered agents that improve execution visibility, cut down manual tasks, and standardize workflows. These efforts allow NVIS to grow quickly and with high quality. Join us to bring up, validate, optimize, and upgrade large-scale AI Factory infrastructure for some of the worlds most advanced accelerated computing environments!

What you'll be doing:

Compose, build, and productionize agentic AI solutions, tools, and applications for the NVIS delivery organization.

Develop LLM-based agents, skills, tool-calling workflows, orchestration logic, backend services, APIs, data pipelines, and automation features as part of NVIS Central.

Translate field, delivery, operations, and product needs into clear technical builds, agent workflows, and working software.

Develop agents that can reason across project data, knowledge bases, operational systems, logs, reports, and delivery workflows.

Build workflows that help NVIS teams identify risks, summarize project status, automate repetitive tasks, improve readiness visibility, and simplify handoffs.

Work with timely engineering, retrieval-augmented generation, context management, agent memory, function calling, evaluations, and guardrails to build reliable AI systems.

Integrate LLMs and agents with internal systems, project data sources, knowledge repositories, reporting tools, and operational workflows.

Collaborate closely with software developers, architects, product managers, DevOps/SRE, and NVIS field teams to successfully implement reliable and scalable solutions.

Contribute to engineering guidelines, including code quality, testing, CI/CD, observability, documentation, security, and production support.