לוח משרות דרושים AI Engineer

Required AI Platform Engineer
Position Overview:
We're assembling a small-scale team of innovators committed to a transformative mission: advancing generative AI from conceptual breakthrough to tangible product reality. As an AI Platfrom Engineer, you will be a critical architect of the technological infrastructure that brings our most ambitious GenAI concepts to life, transforming our digital intelligence solutions through cutting-edge AI innovation.
Build advanced AI platform that operates both as a cloud SaaS and as a fully self-contained on-prem / edge deployment, designed for privacy-sensitive and security-critical environments, at the intersection of backend development, AI integration, DevOps, and open-source systems engineering.
You will be part of the core team responsible for adapting, hardening, and operating our SaaS architecture in on-prem and single-node environments (on Prem servers, laptops). Working closely with architecture and product management and play a key role in making complex AI systems deployable, reliable, and operable outside the cloud.
Key Responsibilities
Platform & Application Engineering:
Adapt cloud-native AI services to on-prem and edge deployments (single node, no managed cloud services).
Build and maintain full-stack components: Backend APIs (Python / Node.js), Lightweight UIs or internal tools when needed, Ensure services are stateless, configurable, and portable across environments.
AI & Open-Source Integration:
Integrate and operate open-source LLMs for:
RAG pipelines
Agentic workflows
Tool calling and orchestration
Work with: Embedding models, Vector databases (local and embedded modes), Analytical engines (e.g., embedded SQL / columnar systems), Optimize inference for CPU / single-GPU environments (quantization, batching, caching).
DevOps & Runtime Engineering (Strong Focus):
Package services into portable Docker containers usable in:
On-prem servers (Kubernetes)
laptop / edge devices
Implement in-process scaling strategies (worker pools, task queues, batching).
Build simple, reliable deployment and startup flows (no heavy orchestration).
Manage configuration, secrets, logging, and observability in constrained environments.
Systems & Reliability:
Design for: Offline operation, Limited resources, Predictable performance
Implement graceful degradation between: SaaS mode, On-prem server mode, Single-node / laptop device mode, Debug complex interactions across AI models, storage, and runtime systems.

Were looking for a highly skilled AI Engineer to join our Forensic Research Group and help shape the next generation of intelligent forensic platforms.
Youll work at the intersection of Generative AI, agentic systems, and real-world data, building advanced capabilities for both internal tools and production systems that transform how complex data is analyzed and understood.
Using state-of-the-art models and emerging AI paradigms, youll design systems that move beyond traditional pipelines into autonomous, adaptive workflows.
Youll help define the future of agentic decoding - where AI doesnt just assist, but actively reason, explore, and uncover critical evidence, impacting millions of cases worldwide.
If youre excited about building impactful AI systems and solving complex, messy problems, this role is for you.
What Youll Do:
Design and build AI-driven and agentic solutions to parse, analyze, and interpret data from forensic extractions (including mobile devices)
Develop GenAI-powered capabilities for understanding, structuring, and reasoning over unstructured data
Combine LLMs, multi-agent systems, and heuristic approaches to improve data identification, classification, and insight generation
Build autonomous and semi-autonomous pipelines that transform manual and brittle workflows into scalable, intelligent systems
Work with real-world, noisy datasets to uncover patterns, anomalies, and meaningful structures
Collaborate closely with forensic researchers and engineers to translate domain expertise into AI-native solutions
Influence system architecture, tooling, and the evolution of internal AI platforms and agent frameworks
Evaluate, fine-tune, and integrate state-of-the-art models (LLMs, multimodal models, etc.) for complex real-world problems.

Required AI Architect
Position Overview:
As the AI Architect for our SaaS Platform & Products, you will define and drive the end-to-end AI architecture that powers our cloud AI platform and product capabilities, while ensuring strong alignment with security, privacy, reliability and cost-efficiency requirements. You will partner closely with engineering leaders, product management, data science, security and operations to translate business needs into a scalable, governed and AI platform that accelerates innovation across multiple product lines.
This is a hands-on architecture role: you will set technical direction, run architectural reviews, and build prototypes to validate technology choices and de-risk delivery.
Key Responsibilities:
Own the AI Vision: Take end-to-end ownership of the AI architecture across, including our cloud AI platform, agentic AI infrastructure, and on-prem/hybrid AI deployments.
Architect an AI Platform for SaaS Products: Define reference architectures and shared building blocks (e.g., AI gateways, orchestration runtimes, memory/RAG systems, vector search, evaluation frameworks, observability, and guardrails) that can be adopted consistently across products and teams.
Cost Optimization and Scalability: Provide architectural leadership to ensure AI systems are designed for cost efficiency and scalability, actively identify and implement opportunities to optimize cloud spend (FinOps), improve utilization, and meet latency/SLO targets.
Product-Centric Collaboration: Collaborate with engineering, product, data science, security, and operations teams to translate product requirements and business needs into a strategic AI architecture that drives measurable product value.
Technology Evolution and Prototyping: Evaluate new technologies, tools, and methodologies to continuously improve our AI systems, build prototypes and proof-of-concepts to validate approaches and accelerate decision-making.
Technical Reviews, Standards and Mentorship: Lead technical design reviews and provide guidance on best practices and emerging technologies. Act as a technical authority and mentor, fostering a culture of engineering excellence and pragmatic delivery.
AI Governance, Privacy and Security: Ensure AI capabilities comply with data privacy, security, and ethical AI guidelines. Partner with security and legal stakeholders to implement governance controls and risk mitigations appropriate for regulated environments.

We're hiring for a new AI Engineering team in Tel Aviv, and you would be the first infrastructure hire. You will own the platform layer for AI agents the team builds: deployment architecture, observability, and production reliability.
The team's first two projects: an agent that automates internal governance processes (vendor reviews, security questionnaires, tool provisioning), and an agent that helps engineering teams prepare for architecture reviews. Both integrate with external APIs (LLM providers, OneTrust, ServiceNow), handle structured decision logic, and manage sensitive data flows with audit requirements.
Highlights
- Greenfield, but with real constraints. You're building on Azure/AWS with enterprise security requirements. The challenge is designing deployment and observability for LLM-backed services. You need to track output quality, cost per invocation, and model drift.
- Enterprise complexity, startup autonomy. Ownership and greenfield environment of a startup, with the integration challenges of a Fortune 200: connecting AI services to real enterprise systems.
- More than infrastructure. Your core is SRE, but you'll also write agent code in TypeScript and Python, work with data pipelines, and ship features alongside the team.
What the Work Looks Like
AI Service Infrastructure - Design and maintain deployment and release infrastructure for AI agents. The stack is cloud-native (Azure/AWS), with services that call LLM APIs, connect to enterprise systems, and handle structured data.
Observability & Reliability - Build monitoring and observability for AI services. Ensure model response quality doesn't degrade silently by tracking errors, logging cost spikes, and monitoring upstream API changes.
Security & Compliance - These agents handle sensitive workflows with elevated security requirements. You will work with our company's security team on standards, but you own how they're implemented in the infrastructure.
Developer Experience - Create tooling that makes it easy for the team to build, test, and deploy. The patterns you set become the team's defaults.

We're looking for an AI and Automation Engineer to join our Automation team and help build the internal tools, AI agents and automation systems that power how we operate.
This role combines hands-on engineering with impactful product development. The work is split between writing code (TypeScript, React, Python) and designing and implementing automation workflows using low-code platforms.
You'll take part in building intuitive internal web applications that teams enjoy using, developing AI-driven solutions that streamline repetitive processes, creating insightful dashboards to support decision-making, and integrating systems that help the business run smoothly and efficiently.
You'll work alongside our Tech Lead and a US-based IT Automation Engineer, contributing across the full spectrum from designing React-based internal tools to building RAG pipelines to orchestrating multi-step business workflows.
What You'll Do
Design and build internal web applications using TypeScript and React that serve teams across the organization.
Create intuitive interfaces that make AI capabilities and automation outputs accessible to non-technical users.
Build and maintain RAG pipelines - including document processing, embedding, vector storage, retrieval, and evaluation.
Work with LLM APIs (Claude API, OpenAI API, AWS Bedrock, Vertex) and implement prompt engineering patterns (tool/function calling, structured outputs, few-shot) with proBuild connectors, APIs, and data flows that keep systems in sync and processes running smoothly.
Work with data from across the business to support decision-making.
Design and maintain automation workflows using low-code/no-code platforms (Workato).
Integrate AI solutions into core business tools - Slack, Jira, Salesforce etc.

We are looking for a hands-on GTM AI Engineer to design, build, and deploy AI-powered workflows and automations across our go-to-market (GTM) systems.
This role sits at the intersection of business operations and technology, and acts as the engine behind revenue intelligence, process automation, and scalable pipeline generation systems.
You will be a technical force multiplier for our GTM team including creating workflows, agents, and integrations, embedded directly into core business systems (e.g., CRM, marketing automation, support tools).
This is not a traditional software engineering role, but it requires strong technical execution and ownership across the full lifecycle.
What youll do:
Design, build, and maintain production-grade AI workflows and automations to streamline GTM execution (e.g., lead enrichment, routing, research, follow-ups, CRM hygiene, and customer handoffs)
Develop and deploy LLM-based solutions, including prompt-driven workflows, reusable prompt libraries, and AI agents embedded within business systems
Build and maintain integrations and data pipelines across CRM, marketing automation, enrichment tools, and internal systems to ensure reliable, connected data across the funnel
Own workflow performance, review success metrics, iterate and demonstrate business impact.
Build and maintain AI-assisted pipeline inspection tools to give RevOps and Sales Leadership real-time visibility into deal health
Identify opportunities to reduce manual work and improve efficiency across RevOps, Sales, Marketing, and Customer-facing teams
Partner with cross-functional stakeholders to map processes, identify bottlenecks, and prioritize high-impact automation opportunities
Drive adoption through enablement: documentation, training, and ongoing feedback loops
Evaluate and implement new tools and technologies based on ROI, scalability, and maintainability.

Required AI Engineering Team Lead
Tel Aviv-Yafo, Gush Dan, Israel
We offer the industrys only platform that fuses customer identity and anti-fraud solutions - customer identity management, identity verification, and fraud prevention.
We sell to industries with large, consumer-facing businesses such as: banking, financial services, insurance, fintech, gaming, ecommerce/retail, telco / media, utilities, etc.
About the Role:
As the AI Team Lead, you will guide a team of highly skilled machine learning engineers in developing and deploying advanced AI/ML solutions that power our identity and security products. Youll combine technical depth with leadership skills to drive innovation, ensure delivery of high-impact projects, and scale our data-driven capabilities across the organization.
This role requires both strategic thinking and hands-on expertise. Youll be responsible for shaping the data science roadmap, mentoring a growing team, and collaborating with product, engineering, and business stakeholders to translate business challenges into practical machine learning solutions.
What youll do:
Leadership & Team Management:
Lead, mentor, and grow a team of machine learning engineers and data scientists.
Foster a culture of technical excellence, collaboration, and continuous learning.
Define team goals, measure progress, and ensure high-quality deliverables.
Strategy & Roadmap:
Own the data science strategy and align it with company objectives.
Identify opportunities for applying machine learning and generative AI across products and internal systems.
Evaluate emerging technologies, tools, and methodologies to keep the team ahead of the curve.
Hands-On Technical Work;
Design, prototype, and implement ML models, including LLM-powered copilots, retrieval systems, and fraud detection pipelines.
Guide the deployment of models into production environments with scalability and reliability in mind.
Ensure best practices for experimentation, evaluation, and monitoring.
Cross-Functional Collaboration:
Work closely with engineering to integrate ML components into production systems.
Partner with product managers to align solutions with customer needs and business priorities.
Collaborate with security and compliance teams to ensure ethical and secure use of data.

Required AI Backend Engineer
Tel Aviv-Yafo, Gush Dan, Israel
We offer the industrys only platform that fuses customer identity and anti-fraud solutions - customer identity management, identity verification, and fraud prevention.
We sell to industries with large, consumer-facing businesses such as: banking, financial services, insurance, fintech, gaming, ecommerce/retail, telco / media, utilities, etc.
About the Role:
As the AI Backend engineer, you will join a team of highly skilled machine learning engineers in developing and deploying advanced AI/ML solutions that power our identity and security products. Youll utilize technical skills to drive innovation, ensure delivery of high-impact projects, and scale our data-driven capabilities across the organization.
This role requires both strategic thinking and hands-on expertise. Youll be responsible for shaping the data science roadmap, mentoring a growing team, and collaborating with product, engineering, and business stakeholders to translate business challenges into practical machine learning solutions.
What youll do:
Design, develop, and maintain backend services for AI agents and tool integrations using latest technologies
Build scalable APIs and microservices that interface with LLMs and AI frameworks
Implement agent orchestration systems, tool calling mechanisms, and workflow engines
Optimize performance and reliability of AI-powered applications at scale
Develop data pipelines for training, evaluation, and monitoring of AI systems
Integrate with various LLM providers (OpenAI, Anthropic, etc.) and manage API interactions.

We are seeking an R&D Lead to lead the development for Agentic Commerce, a brand new product. The product focuses on owning the visual and semantic layer that AI agents rely on to understand, rank, and present products. We are a small, fast-moving, and highly independent team with full end-to-end ownership. The opportunity ahead is large: the front door of commerce is shifting from websites to AI agents, and merchants must adapt to be discovered, selected, and converted inside agent-driven experiences.
Responsibilities
Lead and be accountable for all development efforts, with a co-founder / CTO-level mindset
Co-lead the pod alongside the GM as a true counterpart
Build a deep understanding of how AI agents consume product data, media, and signals, and how merchants win selection and conversion in this new paradigm
Starting as a solo developer, you will be responsible for designing and shipping an AI-native product, growing a team when needed
Leverage platform to get to market quickly, building only what meaningfully advances learning and traction
Continuously adjust technical direction as agent behaviors, protocols, and market assumptions evolve

Were looking for a highly motivated AI Developer to help design, build, and deploy intelligent agentic systems across our product ecosystem. In this role, you'll work at the intersection of machine learning, backend systems, and modern frontend technologies to deliver AI-first features that feel magical to users.
This is a hands-on, cross-functional role ideal for engineers who love building full-fledged features-from data pipelines and LLM orchestration to intuitive UI experiences-with a strong product mindset.
Responsibilities:
AI Agent Design & Integration
Design and implement autonomous or semi-autonomous agents using LLMs (e.g., OpenAI, Anthropic, open-source models).
Work with prompt engineering, RAG pipelines, and tool/plugin integrations to enable agents to interact with internal and external systems.
Build scalable agent runtimes and orchestration layers (e.g., LangChain, Semantic Kernel, ReAct-based agents).
Fullstack Product Development
Own full-stack features end-to-end: from backend APIs and data models to React-based frontend interfaces.
Integrate AI/agent capabilities into customer-facing products with clean UX and measurable performance.
Collaborate closely with design, product, and data teams to bring ideas from concept to production.
Systems & Infrastructure
Build and maintain backend services and pipelines that support AI agents, including vector search, embeddings, function calling, and observability.
Optimize inference flows for performance and cost, potentially using streaming, caching, or local model inference.
Ensure systems are secure, reliable, and compliant with InfoSec standards.
Experimentation & Continuous Improvement
Rapidly prototype and iterate on new AI capabilities and user experiences.
Analyze performance and usage metrics to drive product and model improvements.
Stay up to date with the evolving AI toolchain and emerging agent architectures.