דרושים » הנדסה » AI Enterprise Solution Expert

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 a well-funded, early-stage startup building our Founding Solutions Engineering team to help build and own our enterprise customer motion from the ground up. This role is perfect for a hands-on, technically deep builder who thrives in fast-moving environments, enjoys making things work in the real world, and partners closely with founders, Product, and R&D to deploy and scale complex, AI-driven enterprise solutions.

You will own the technical side of the post-sale customer journey, from deployment and integration through adoption, expansion, and ongoing success, while helping define how our delivery function evolves as we grow.

Your Impact
Customer Onboarding & Process Analysis

Engage with customers post-close to understand workflows, operational processes, and deployment requirements.

Translate findings into clear implementation plans for AI-powered solutions.

Data, AI Models & Workflow Development

Partner with customers to assess data readiness and gather inputs for AI workflows.

Configure and integrate AI components into tailored, AI-driven workflows.

Collaborate with R&D to close data gaps and extend product capabilities when needed.

Deployment & Proof of Value

Own end-to-end solution deployment and ensure models meet performance and business value expectations.

Define success metrics and drive adoption post-launch.

Integration, Security & Rollout

Lead integrations with enterprise systems (e.g., Finance, ERP, email platforms), ensuring scalability and security.

Guide customers through security, compliance, and IT approval processes.

Plan and execute rollout to drive smooth implementation and adoption.

We're looking for an AI Builder to join our Marketing team- someone who will design, build, and ship AI-powered tools and workflows that make marketing faster, smarter, and more effective. This role reports to the VP of Marketing.
If you're a creative, self-driven builder who moves seamlessly from ideation to execution, this role was made for you. You thrive in dynamic, fast-moving environments, take full ownership of your projects, and don't need hand-holding to turn an idea into a working tool. You'll be the team's go-to AI owner - collaborating closely with marketing stakeholders while also pursuing your own ideas, embracing a fail-fast mindset to try, scale, or kill initiatives quickly.
What will you do?
Own marketing AI tools: Fully own, build, and maintain all AI-driven tools that support the marketing team.
Lead lead-generation initiatives: Drive growth-hacking and meeting-generation pipelines end to end.
Build inbound demand: create lead magnets, interactive AI-powered assets, an SEO/AEO content engine, and on-site personalization that turn visitors into pipeline.
Build outbound demand: own list building, data enrichment, intent-signal monitoring, and 1:1-at-scale outbound sequences that create net-new pipeline.
Run an AI creative pipeline: produce video, image, and copy at volume with AI tools, giving marketing a scalable creative engine.
Educate the team on AI: Teach and keep the marketing team up to date on the latest AI trends and capabilities.
Build collaborative and independent tools: Partner with the team to build impactful day-to-day tools while also pursuing independent AI initiatives.
Unblock the AI wishlist: Address the marketing team's backlog of AI ideas that they haven't had time to implement.
Experiment and iterate quickly: Apply a fail-fast approach, testing new tools and initiatives, then scaling or killing them based on results.

This role owns the AI-native GTM engine: the systems that find, enrich, reach, and route prospects and customers, and the workflows that create new opportunities for the company. You build revenue systems with AI tooling, then run them against pipeline targets. The scope is wide on purpose. You own the motion and the measurement, so theres no gap between what you build and what you can prove.

What youll own
Outbound engine. Multi-channel outbound end-to-end: list building, segmentation, messaging, sequencing, launch, and A/B iteration across email and LinkedIn. AI-driven personalization at scale, moving prospects from first touch to booked meeting with minimal manual input from sales.
Inbound conversion. Everything between the first visit and the booked meeting: on-site conversion paths, forms, chat, scoring, routing, and speed-to-lead. Instrument every step, find where intent leaks, and close the gap with automation instead of headcount. No inbound lead should sit waiting for a human to qualify it.
CRM truth and attribution. The measurement layer under everything else. If outbound, inbound, AEO, and content cant be attributed, none of it can be optimized or defended. Own data quality across HubSpot and Salesforce.
Data and enrichment. Prospect identification and signal tracking: enrichment waterfalls, buying signals, social listening, and clean push architecture into the CRM.
AI agents and automation. Claude is a teammate here, not a chatbot. Build the agents, skills, and workflows that encode our GTM playbooks and kill anything manual.
AI content and discovery. Build the writing agents that produce inbound at volume: programmatic pages, competitor comparisons, persona and vertical landing pages, localized variants, and one asset turned into fifteen across channels. AEO is the other half of this. Buyers now start in ChatGPT, Claude, Perplexity, and AI Overviews, not on a results page, so the content has to be built to be retrieved and cited: structured content and schema, presence on the review sites and communities models pull from, and a technical layer that makes our legible to crawlers.
Customer expansion. The signal engine that finds revenue inside the base: segmentation, expansion triggers, and routing each account to the right owner or the right sequence at the right time.
The stack itself. The GTM tech stack end-to-end: evaluate, buy, integrate, and kill tools as the motion evolves, and build tools with direct impact on revenue when nothing off the shelf does the job.

Are you an analytical person who is deeply interested in AI and LLM technology?
Is your superpower accurately categorizing information and identifying patterns?
Do you love to solve interesting challenges and make a direct impact through your work?
Join our Tel Aviv team as an AI Data Analyst and help shape the future of AI. We're looking for analytical minds to contribute to our cutting-edge Large Language Model (LLM) project. In this unique role, you'll be performing LLM data quality tasks, ensuring consistency and quality in our conversations and helping design and support our flagship products.
This is more than a traditional data-labelling role. You will combine hands-on evaluation work with analytical thinking to help shape how our AI experiences evolve.
Sound like your perfect fit? We want to hear from you!
As an AI Data Quality Analyst, you will evaluate LLM-generated conversations and other text-based data, identify trends and regressions, and partner with AI Engineers and Product Managers to improve model performance. In this role, you will:
Review and evaluate LLM-generated conversations and other text-based outputs against task-specific quality criteria and success metrics.
Perform high-quality annotation and data labeling to create reliable datasets for model training, benchmarking, and evaluation.
Monitor real production data to identify quality trends, regressions, and areas for improvement.
Identify qualitative patterns, recurring failure modes, regressions, and opportunities to improve model behavior and user experience.
Analyze evaluation results and clearly communicate insights, risks, and recommendations to cross-functional stakeholders.
Work closely with our AI Engineers and Product Managers to understand the data needs and ensure data quality processes align with goals, ensuring Paradox and our company products remain top in class.

We are constantly striving to make our systems reliable, scalable, and simple to operate so our services are available to travelers when they need them most. With our continued growth, we have exciting challenges ahead and we're looking for a Senior Site Reliability Engineer to join our team in Tel Aviv. This role blends classic SRE ownership with pragmatic AI SRE work: you will build and operate the platforms, automation, observability, and incident response practices that keep Navan reliable, while helping teams use AI solutions, AI providers, and their APIs safely and dependably.



This is a hands-on engineering role, not a research role. You will partner with product, platform, data, security, support, and incident response teams to make production systems and AI-powered experiences more resilient. You will use software engineering, infrastructure as code, SLOs, telemetry, provider observability, and automation as your main tools, and you will apply AI where it creates measurable reliability value rather than novelty.



This position is based out of our new Tel Aviv office.



What You'll Do:

Support AI-based application solutions where reliability matters. Partner with the development teams building AI-powered travel experiences to support the development and production operation of their solution.
Work with AI solutions, providers, and APIs. Partner with teams integrating AI capabilities and providers, with attention to API reliability, authentication, quotas, rate limits, latency and provider-specific operational constraints.
Troubleshoot AI tools and provider issues. Diagnose failures across AI-powered workflows, provider APIs, configuration, permission errors, degraded responses and related areas.
Operate reliable production platforms. implement and run cloud infrastructure,and help product teams move quickly without compromising reliability.
Improve observability. Build dashboards, alerts, traces, logs, and runbooks that make service health clear, actionable, and tied to SLOs and customer impact.
Apply AI to SRE workflows. Prototype and productionize AI-assisted systems that create effective and efficient operations
Automate operational toil. Create tools, workflows, and automation that remove repetitive manual work and make operational knowledge easier to use.

We are looking for a mission-driven BizOps Engineer to join our R&D team!
We specialize in advanced AI predictive models, focusing on LTV, churn, and intent to drive business growth.
As the BizOps Engineer, you will serve as both a platform architect and a hands-on builder. You will own the AI-native platform architecture and governance for all non-core product functions, including Sales, Operations, HR, Legal, and Finance, while also building, maintaining, and scaling custom AI solutions yourself to address specific departmental needs.
Reporting directly to the VP of R&D, this role begins as a high-impact Individual Contributor (IC) with a clear mandate to build and lead a dedicated engineering team as the function scales. Your primary focus will be designing and deploying infrastructure and security protocols while simultaneously delivering the bespoke AI tools that empower teams and drive operational efficiency.
You will have the autonomy to choose the most effective path forward for any given challenge, whether that means leveraging existing SaaS platforms, integrating open-source projects, or building custom solutions from scratch, always guided by the specific needs and projected ROI of the departments you support.
Responsibilities:
Architect and maintain the internal AI-native platform, while personally building and scaling custom AI solutions and automated workflows for cross-functional teams.
Establish and enforce security protocols, governance frameworks, and best practices to enable safe and compliant AI solution development across the organization.
Identify and scale platform enablement opportunities that allow departments to automate their own workflows using approved AI capabilities.
Monitor and manage organizational compliance for internal AI applications, ensuring data privacy and ethical standards are met at scale.

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.