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

The role offers significant business impact, transforming complex challenges into scalable AI-powered products and solutions used in large-scale production environments. Key Responsibilities: Lead the end-to-end architecture, design, development, and deployment of advanced Generative AI applications, including Multi-Agent Systems and Retrieval-Augmented Generation (RAG) solutions. Design and optimize RAG pipelines, integrating Large Language Models (LLMs) with structured and unstructured data, including Knowledge Graphs and Vector Stores. Build, manage, and automate Generative AI solutions using AWS services, including Bedrock, S3, SageMaker, Lambda, and Step Functions. Lead Proof of Concepts (POCs) and evaluate emerging GenAI technologies and products to identify the most suitable solutions for business needs. Integrate AI solutions with existing enterprise systems using APIs and Microservices. Collaborate with data Engineers, Analysts, and Product Managers to translate business requirements into scalable and practical AI solutions. Implement advanced MLOps processes, including CI/CD, Docker, and monitoring solutions, to ensure the reliability, scalability, and maintainability of AI applications.

We are a well-funded, early-stage startup looking for a talented and motivated Backend Engineer to join our founding team. The focus of this role is to design, develop, and deploy autonomous AI agents that can automate and optimize complex enterprise workflows. You will work on building intelligent systems capable of decision-making, data extraction, document processing, and other tasks typically requiring human intervention. This is an opportunity to influence the architecture and strategy of AI-driven automation solutions for large-scale enterprise environments.

Your Impact
AI Agent Development

Design and develop autonomous AI agents using best of breed large language models to automate tasks such as document processing, data extraction, and workflow management.

Implement reinforcement learning techniques to enhance decision-making capabilities of AI agents.

AI Integration

Build and integrate APIs that connect AI agents with external systems and enterprise software (ERP, CRM).

Use frameworks like TensorFlow, PyTorch, or Hugging Face to deploy and optimize AI models for real-time processing.

Data Processing and Pipelines

Design and manage data pipelines to process and analyze large volumes of documents and unstructured data efficiently.

Optimize data handling to improve speed and accuracy of AI agents.

Security and Compliance

Implement authentication and authorization mechanisms to secure AI-driven systems.

Ensure compliance with data privacy standards (e.g., GDPR, HIPAA) and adopt best practices for secure data handling.

Cloud Infrastructure and Scalability

Deploy AI agents on cloud platforms (AWS, GCP, or Azure) ensuring scalability and reliability.

Leverage containerization (Docker, Kubernetes) for efficient deployment and management.

Testing and Optimization

Develop and execute unit, integration, and performance tests for AI-driven systems.

Continuously monitor and optimize system performance for speed, accuracy, and cost-efficiency.

Collaboration

Work closely with AI researchers, front end engineers, and product teams to align AI agent capabilities with business requirements.

Participate in code reviews, design discussions, and architecture planning to drive innovation.

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.

The AI Forward Deployed Engineer (FDE) is responsible for driving the successful adoption of AI solutions across our Groups business units.

The role focuses on translating business needs into practical AI solutions and ensuring they are effectively embedded into real workflows, delivering measurable business impact.

FDEs work closely with business teams to identify high‑value opportunities, deploy and build customized AI solutions, and continuously improve outcomes. Success is measured by adoption, usage, tech quality and business value.


Key Responsibilities
Partner with business units and tech teams to identify and prioritize AI use cases.
Own AI initiatives end‑to‑end: from problem definition to deployment and impact measurement.
Deploy and adapt AI solutions to fit real operational workflows.
Drive user adoption through training, iteration, and feedback.
Support continuous improvement and scaling of successful solutions.

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 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 a leading provider of sales engagement and revenue optimization solutions, empowering businesses to streamline their sales processes and drive growth. As a company that advocates a customer-first approach, we're known for our professional Success team and are hiring an AI Solution Expert. As an AI Solution Expert, you will serve as the technical trusted advisor for our customers, bridging AI capabilities with their specific business goals and revenue workflows. You will embed deeply with customers. Part consultant, part engineer, part strategic advisor: leading AI sessions, configuring AI-driven workflows using our MCP, training users on best practices, and ensuring measurable business outcomes. You'll collaborate closely with Customer Success, Product, Support, and Engineering teams, and will be the go-to expert for any AI-related technical solution. This is an ideal role for someone who thrives at the intersection of cutting-edge AI technology and real-world business challenges. This role is ideal for someone who thrives on ambiguity, moves fast, and finds equal satisfaction in writing clean code and presenting a solution to a CFO. Key Responsibilities
* Embed deeply with customers to understand their technical environment, business processes, and AI workflows and then design and build solutions that fit.
* Build and deploy AI-powered workflows, MCP-based automations, and intelligent features within the platform tailored to each customer's use case taking into account the integration landscape such as salesforce, HubSpot, Dynamics, NetSuite, and billing platforms.
* Leverage LLM, AI, APIs, webhooks, data pipelines, automation scripts.
* Lead technical AI sessions - review requirements, demo solutions, help build agents and perform training.
* Translate complex AI and technical concepts into clear, tangible business value; operate as a trusted advisor, not just an Implementer.
* Identify and resolve technical blockers proactively; operate with confidence under ambiguity and incomplete information.
* Build reusable engineering assets: integration libraries, prompt templates, and AI workflow scaffolding that scale across the customer base.
* Act as a direct feedback loop from the field to the Product and Engineering teams surfacing patterns, edge cases, and AI feature opportunities discovered during deployments.
* Monitor post-go-live and drive continuous improvement.