לוח משרות דרושים הייטק AI , לחיפוש זה נמצאו 313 משרות

We're looking for a Senior AI Product Manager to take ownership of core areas of our B2B platform - the point where powerful AI capabilities become real products that customers rely on. We've already built the foundation: rich data, agentic infrastructure, and deep domain intelligence. Now we need someone who can take what's working with one customer and make it work everywhere - productizing capabilities, scaling them across our customer base, and raising the quality bar as they grow.

This is a hands-on builder role. You'll work in close, direct partnership with both customers and R&D - running discovery, analyzing the data, and prototyping ideas yourself, then teaming up with R&D to bring them to life at scale.

This is a fast-moving area, and we'll shape the exact scope together, based on where you can create the most leverage for the business. We're looking for true startup reflexes: the ability to shift focus as priorities shift, zero in on what matters most this quarter, and make clear, deliberate trade-offs.

If you thrive in a fast-paced startup environment and want to build AI products that make complex insights accessible and actionable for a real-world, >$1B industry, this role is for you.

What You'll Do

Own core areas end to end - strategy, discovery, execution, and measurement. Define what success looks like in numbers, and stay accountable to it well beyond launch.
Productize and scale what works. Turn capabilities proven with one customer into products that work seamlessly across your entire customer base.
Lead discovery yourself. Run customer and prospect conversations to uncover what people will actually pay for, and bring back a scoped, evidenced bet.
Prototype your ideas. Go from concept to working prototype using coding agents and AI tooling, with a design bar high enough to serve as a real proposal.
Own quality. Define what "good" means for our agents, build the evals to measure it, and raise the bar on reliability, cost, and trust as the system evolves.
Partner deeply with R&D. Engage on technical trade-offs - accuracy, latency, cost, build vs. buy - and earn the team's respect through substance.
Drive it to market. Team up with design, sales, marketing, and customer success to package, position, and launch it, then feed adoption data back into the roadmap.

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

We are looking for a professional to drive AI and business transformation initiatives, combining strategic thinking, execution discipline, and strong cross-functional stakeholder management.
This role will play a key part in shaping and driving AI-enabled transformation across the organization, working closely with business, product, AI, and IBT teams to translate strategic priorities into clear execution plans, adoption frameworks, and measurable business impact.
The role will be responsible for driving transformation execution governance, including initiative prioritization, business process understanding, dependency management, meeting outcomes, follow-up cadence, risk tracking, and cross-functional accountability across strategic initiatives.
Responsibilities
Drive AI and business transformation initiatives from strategy into structured execution.
Work closely with business, product, AI, and IBT stakeholders to shape AI-driven capabilities and adoption processes.
Lead transformation execution governance, including planning, prioritization, follow-up, dependency management, and execution rhythm.
Translate business priorities into actionable plans, clear workstreams, and measurable outcomes.
Define KPIs, ROI models, and cost-related considerations to support decision-making and value realization.
Identify risks, gaps, dependencies, and blockers, and ensure timely escalation and resolution.
Build and implement transformation processes, governance structures, and execution frameworks.
Support senior stakeholders with clear communication, structured updates, and presentation materials.
Drive cross-functional alignment and accountability across multiple teams and initiatives.

Were hiring a Senior AI Analyst to join our Research team and help shape our AI-driven cybersecurity platform. In this role, youll dig deep into how our AI agents perform, uncover whats working and where the opportunities are, and turn that analysis into insights that shape the roadmap, strengthen the product, and drive customer value and growth.

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.

As our QA Engineer, you'll build QA infrastructure that ensures our product meets the highest bar, and youll be the first voice advocating for quality at every stage of development.
About The Role
This is a hands-on, high-impact role. You'll work tightly with developers, product managers, and designers to catch bugs before they ship - and to build the processes and automation that prevent them from appearing in the first place.
You'll design and execute comprehensive test strategies across our platform's most critical workflows, from escalation rules and automation audit configurations to the AI agent behaviors that power our customers' support operations.
What You'll Bring
A quality-first mindset: you see QA not as a gate at the end of the pipeline, but as a discipline embedded in every phase of product development.
The ability to make sharp risk-based decisions - you know when a 2% repro-rate bug in a non-critical feature is a release note vs. a release blocker.
Experience establishing or maturing QA processes in teams that may not have had formal QA before.
Comfort navigating the tension between speed and quality - you can articulate trade-offs to stakeholders and push back on pressure to skip testing when the risk warrants it.
A collaborative, empathetic approach to working with developers - you build trust, not friction.
What You'll Do
Design, maintain, and execute comprehensive test suites - including functional, regression, integration, smoke, performance, and exploratory tests - across our company's AI platform.
Build and expand automated test coverage using Playwright and Typescript, integrated into our CI/CD pipeline.
Collaborate with developers throughout the SDLC: participate in sprint planning, review requirements for testability, join design reviews, and validate acceptance criteria before development begins.
Own the QA process for key product areas, including escalation configurations, automation audit rules (email patterns, subject lines, message keywords), and AI agent behavior customization.
Investigate, document, and triage bugs with detailed, reproducible reports - and advocate for the right balance between blocking a release and shipping with known issues.
Debug web application issues using browser DevTools, network analysis tools, and API testing utilities.
Champion a testing culture across the engineering organization - quality is everyone's responsibility, and you help make that real through process, tooling, and collaboration.
Proactively identify subtle bug classes - race conditions, off-by-one errors, timezone issues, Unicode handling, caching inconsistencies - and build test strategies specifically targeting them.
Evaluate and recommend improvements to QA tooling, processes, and infrastructure as the team and product scale.

A full-time Applied AI Engineer role focused on building and optimizing our LLM-powered clinical compliance engine. You will design, implement, and refine AI systems that analyze clinical documentation, detect compliance issues, and generate actionable insights for healthcare providers.
Key Responsibilities
Design and implement LLM-based rule engines for clinical compliance detection
Build RAG systems and semantic search pipelines for clinical documentation
Optimize prompts, fine-tune models, and evaluate AI system performance
Collaborate with clinical experts to translate regulatory requirements into AI workflows
Monitor and improve AI accuracy, latency, and cost efficiency in production
Develop evaluation frameworks and testing strategies for AI outputs.

Were looking for a strong AI engineer with a builder mindset to lead the tech work on our LLM/Agentic flows.
What we do:
AI vertical software for companies that design and build interior spaces.
0>$2M ARR in less than a year.
Building from scratch the industry tech stack - at the core, 3D engineering/design software that connects to customer workflows and catalogs and is built to enable agent led design.
Job Responsibilities:
Lead evaluations & experimentation of new LLM/agentic features.
Find creative solutions to hard problems.
Build and optimize.

-6+ years of experience in machine learning engineering or applied AI, with a preference for an MSc or PhD in a relevant field.
Hands-on experience building and deploying large-scale production systems with machine learning components.
Practical experience with modern AI architectures, including LLMs, RAG pipelines, vector search, and prompt orchestration.
Strong backend engineering experience and familiarity with building production services.
Experience implementing and productionizing AI frameworks with automated testing, monitoring, and data quality controls.
Strong architectural thinking and algorithmic intuition for model selection, evaluation, and optimization.
Ability to work independently in fast-moving environments and take ownership of complex technical challenges.

As an AI Expert at our company, youll take full ownership of AI-powered product capabilities - from concept to production. Youll work closely with Product, Engineering, and Security domain experts to build practical, reliable AI systems that improve how teams operate and secure cloud environments. This is an applied role: we care about shipping, adoption, and measurable outcomes. Youll engage with users, test MVPs in production, iterate quickly based on feedback, and make sure what you build holds up in the real world. And yes youll also jump in as a Support Hero when needed, helping customers succeed and learning directly from how they work.
Key Responsibilities
Build and ship AI features that improve cloud security operations (e.g., control recommendations, drift detection explanations, guided remediation, policy generation, investigations, workflow automation).
Design and implement AI agents tuned for cloud-security tasks, including the surrounding framework (tools, permissions, orchestration, guardrails, and automation).
Apply deep understanding of cloud security domains (IAM, network controls, logging/telemetry, posture management, governance, misconfiguration, threat scenarios) to guide product decisions and ensure the AI behaves safely and correctly.
Integrate with multi-cloud APIs and security controls (AWS/Azure/GCP/OCI), building end-to-end flows from user intent → actionable outcome. Create and maintain evaluation methods for model quality (accuracy, coverage, hallucination rates, latency, cost, and user satisfaction) and build tooling to continuously measure performance in production.
Build data and feedback loops to keep systems relevant over time (user corrections, outcome tracking, regression testing, prompt/model iteration).
Stay current with practical advancements in LLMs/agents and engineering patterns, and adopt whats useful (not hype) into production.

We're building the financial infrastructure that powers global innovation. With our cutting-edge suite of Embedded payments, cards, and lending solutions, we enable millions of businesses and consumers to transact seamlessly and securely. With 900+ employees worldwide and an R&D center of over 160 employees in Jerusalem - were reshaping how financial technology is developed and delivered..
The Role:
As AI capabilities accelerate across the bank, we need an engineer to design and enforce safe AI usage-protecting customer data, preserving model integrity, and meeting our regulatory obligations. You'll be the architect of guardrails, tooling, and policies that make AI both secure and useful for product and internal teams. This isn't about slowing things down; it's about building the trust layer that lets innovation move fast without breaking things.
Who You Are:
You're a security engineer who's excited about the AI wave-someone who sees LLMs and Agentic AI as fascinating puzzles to secure, not just threats to mitigate. You've spent 5+ years in Security Engineering, AppSec, or Cloud Security, and at least 1-2 of those years have been spent getting your hands dirty with LLMs, AI Agents and MCPs. You understand how agentic frameworks (LangGraph, CrewAI, AutoGen, and similar) orchestrate multi-step tool use-and where trust boundaries break down. You've assessed or secured AI-powered coding agents (Claude Code, GitHub Copilot, Cursor) and understand the unique risks of AI with filesystem, terminal, and API access in Developer environments. You're equally comfortable dissecting a prompt injection attack as you are writing a Terraform module or shipping a Python library. You know your way around AWS and/or Azure, modern app stacks ( Python /TypeScript, REST/gRPC, containers/Kubernetes), and can translate security requirements into Developer -friendly tooling-not just PDF policies that gather dust. You communicate clearly in English and Hebrew, thrive in regulated environments, and understand that security in financial services means mapping controls to frameworks like FFIEC, SOC 2, and PCI DSS-and actually having the evidence to prove it.
What Youll Actually Be Doing:
* Design enterprise AI guardrails across Azure and AWS (e.g., Azure AI Studio/Azure OpenAI, Amazon Bedrock/SageMaker): content filtering, PII redaction, prompt/response validation, and policy enforcement services.
* Assess and define secure usage patterns and data governance controls for agentic frameworks and coding agents: permission scoping, tool-call authorization, leastprivileged retrieval, session isolation, and MCP server governance.
* Threat model AI systems (apps, agents, MCPs, RAG, fine-tuning pipelines) using frameworks like STRIDE and the OWASP Top 10 for LLM Apps; define misuse scenarios (prompt injection/context poisoning/jailbreaks/ data exfiltration) and build mitigations.
* Build monitoring and telemetry: privacy-preserving prompt/response logging, sensitive- data detection, safety/eval dashboards, drift/abuse signals, and incident hooks into our SIEM.
* Integrate AI security into the SDLC: reusable libraries, pre-commit checks, CI/CD gates, policy-as-code, and secure-by-default reference architectures for product teams.
* Evaluate thirdparty AI vendors and internal apps: security reviews, data residency and retention requirements, SSO/SCIM integrations, DPA/TPRM inputs, and continuous control testing.
* Partner across Security, data, Privacy, and Engineering to map AI controls to FFIEC, SOC 2, and PCI DSS; document control evidence for audits.
* Lead/participate in AI redteaming: automated jailbreak/promptinjection tests, safety benchmarks, purpleteam exercises, and response playbooks for AI incidents.
* Enable the org with concise guidelines, examples, and training on safe AI development and usage.
 
Why Youll Love Working Here:
* Flexible hybrid.

We are looking for a high-ambition engineer to join our team in Israel and own the
intelligence layer of our platform: the agents that analyze customer codebases, reason about
vulnerabilities, and produce remediations autonomously.
First and foremost, this is a senior software engineering role. You will design and build production services, write code across the stack, and own what you ship end to end - the AI agents are software, and you build the systems they run on. This is not a prompt-tweaking role: you will design multi-step agentic systems end to end - context engineering, tool design, orchestration, evaluation, and production monitoring - on a "zero legacy" stack (Python, Temporal, K8s, and frontier LLMs). We value a low-ego mindset and a passion for solving deep technical challenges in a fast-paced, collaborative environment.

We're looking for an experienced AI Engineer to join our core team and design, build, and implement the autonomous AI agents that power revolutionary approach to eliminating container vulnerabilities at the source.
Responsibilities:
Explore and prototype cutting-edge AI techniques to tackle our hardest challenges
Architect and develop autonomous AI agents that intelligently eliminate vulnerabilities during container image generation
Collaborate with product and engineering teams to bring AI-driven features from concept to production
Monitor and evaluate AI model performance, implementing feedback loops to continuously improve accuracy and reliability
Develop and maintain machine learning pipelines that process security data and generate actionable insights
Ensure AI systems are robust, reliable, and performant at enterprise scale

In this role, you will be a key contributor to the design and implementation of AI Graph Compiler software stack for Neural Processing Units (NPUs). You will take part in defining software architecture, implementing performance-critical components, and enabling efficient execution of advanced neural networks under tight power, memory, and latency constraints.

You will work closely with hardware and system architects, software and hardware engineers, influencing both software and hardware decisions. You will design and implement major parts of NPU embedded solutions, actively promoting AI capabilities to the customers.

What will you do:

Own and design key components of the AI Graph Compiler software stack for NPU-based systems.

Optimize inference performance (latency, throughput, memory footprint, power) for edge deployments.

Collaborate on HW-SW co-design, influencing NPU architecture.

Support IP evaluations and silicon bring-up, root-cause complex HW/SW issues, and influence development methodologies.

Mentor junior engineers and contribute to technical best practices.