לוח משרות דרושים הייטק AI עבודה היברידית, לחיפוש זה נמצאו 39 משרות

We are looking for a technically-grounded, visionary Product Owner to lead GenAI-powered initiatives within our B2B platform. You will stand at the intersection of cutting-edge LLM technology and enterprise-scale business impact, transforming how global financial institutions handle everything from underwriting automation to complex claims intelligence.

Key Responsibilities:
AI Roadmap & Strategy: Define and own the GenAI roadmap, identifying high-value use cases like fraud detection copilots, document intelligence, and agentic underwriting.
Backlog Excellence: Own and optimize the product backlog, translating complex AI concepts into clear user stories and acceptance criteria for engineering and data science teams.
Technical Specs & Orchestration: Read and write technical specs, API contracts, and data models. Youll work closely with Development & architecture teams
Experimental Mindset: Lead lean experiments and A/B tests to validate LLM performance, managing the balance between probabilistic AI outputs and deterministic business rules.
Stakeholder Leadership: Communicate the vision and progress transparently to global stakeholders and enterprise clients, acting as the bridge between "the possible" and "the practical".

Were looking for a Hands-on AI & Data Engineering Manager to lead a team building intelligent, large-scale consumer experiences powered by AI. In this role, you will lead engineers developing AI-driven products, work closely with data scientists, product managers, and designers, and ensure that AI capabilities-from LLMs and agents to product data insights-are translated into reliable, scalable production systems. You will be responsible not only for delivering AI features, but also for analyzing product data and user behavior to continuously improve AI performance and product outcomes. This is a technical leadership role combining engineering management, architecture ownership, AI product development, and data-driven decision making.
Key responsibilities:
Lead a team of data scientists, data engineers and product analysts
Own the delivery of AI-powered product capabilities, from research and experimentation to production and operation
Drive excellence, code quality, and best development practices
Provide technical direction and hands-on guidance for complex AI systems
Drive the integration of LLMs, AI agents, and intelligent workflows into core consumer experiences
Ensure AI solutions are safe, scalable, observable, and continuously improving
Lead initiatives around product data analysis and experimentation
Analyze user interactions with product features to improve accuracy, UX, and business impact
Partner with product teams to define metrics, dashboards, and experiments that guide product improvements
Design system architectures for AI-enabled applications at scale
Evaluate and select technologies for AI platforms and data pipelines
Guide the development of prompt engineering frameworks and centralized prompt management
Ensure robust monitoring, evaluation, and feedback loops for AI outputs
Translate product and business goals into technical roadmaps and execution plans
Drive alignment between AI capabilities and measurable product outcomes.

We're looking for a senior AI Engineer with a strong backend background and deep experience building agentic AI systems to join our growing team.
Responsibilities:
Own the development of end-to-end AI agents, including both customer-facing agents and deep background agents. This includes designing agent architectures, building and maintaining agent tools, constructing and iterating on prompts, and continuously evaluating, operating, and improving the agent infrastructure in production.
Integrate large language models (LLMs) into production environments to enable autonomous task handling and decision-making.
Collaborate closely with engineering, product, and security teams to turn ambiguous problems and AI research into reliable, production-grade capabilities.

Were looking for a Generative AI Developer to join our forward-thinking engineering team. This role is perfect for someone with a passion for cutting-edge AI, a strong software engineering background, and the creative spark to identify and implement novel use cases within our product.
You will play a critical role in adding AI capabilities to our FinOps SaaS platform. Whether it's enhancing user workflows, automating insights, or inventing entirely new product experiences, youll have both the freedom and support to experiment and execute.
We provide a uniquely rich dataset covering the full scope of a companys cloud spend. This expansive data playground offers a powerful foundation for experimentation and insight generation, enabling the development of intelligent, value-driven features.
Responsibilities:
Lead the charge in transforming our product and preparing it for the agentic age.
Design, build, and deploy generative AI-powered features across our product.
Identify opportunities for AI integration by proactively exploring FinOps use cases and user needs
Prototype and validate new AI use cases quickly and iterate based on internal and external feedback
Collaborate cross-functionally with product, design, and backend teams to drive innovation from concept to production
Stay current with the fast-moving generative AI landscape and evaluate new models, APIs, and tools (e.g., OpenAI, Anthropic, Hugging Face, AWS Bedrock, open-source LLMs).
Live in the future and track new innovations and paradigms in this fast evolving field and identify opportunities to integrate them into the product
Implement safeguards, prompt engineering techniques, and usage monitoring to ensure high-quality AI outputs
Optimize model performance, inference time, and cost efficiency within AWS infrastructure.

we are looking for a Senior AI Engineer.
Responsibilities:
- Design and develop LLM-powered security features and internal AI tools, including RAG pipelines, multi-agent workflows, and prompt-engineered systems for cybersecurity use cases
- Architect and operate multi-agent systems in production - covering orchestration, inter-agent communication, task delegation, and failure handling at scale
- Build agent monitoring and observability pipelines, including tracing, drift and failure detection, alerting, and reliability SLA management
- Build and maintain scalable MLOps infrastructure - model serving, evaluation frameworks, experiment tracking, and CI/CD for ML
- Fine-tune and adapt foundation models on internal datasets such as network telemetry, security logs, and threat intelligence
- Establish and champion best practices for model observability, safety, and responsible AI deployment
- Stay current with the LLM/GenAI ecosystem and drive continuous improvements to the AI SDLC and AI Research cycle

Join an innovative technology company developing an AI-powered computer vision platform with real-world impact. In this role, you'll lead the design, development, and deployment of advanced AI and computer vision algorithms, taking solutions from research to production while working closely with multidisciplinary engineering teams.
Responsibilities:
Design, train, and deploy deep learning models for computer vision applications.
Develop algorithms for object detection, segmentation, tracking, and behavior analysis.
Build and optimize real-time AI solutions for edge and cloud environments.
Research, evaluate, and integrate state-of-the-art AI and computer vision technologies.
Monitor, debug, and continuously improve production ML systems.
Collaborate with Software, Product, QA, and multidisciplinary engineering teams, while mentoring other engineers.

Were looking for an Applied AI Engineer to join the Algorithmics team in a hands-on R&D role focused on building production AI systems for visual content creation. The role is less about training models from scratch and more about building intelligent systems on top of existing image, video, and language foundation models.
Youll work at the intersection of research, product, and engineering- building real-world AI capabilities used in production by creative teams.
Responsibilities:
Build AI workflows for visual content using image/video models, LLMs, and agentic systems.
Design orchestration and context-management layers (brand rules, assets, templates, user intent, constraints).
Prototype and validate new capabilities through data-driven experiments (metrics, evaluation sets, A/B testing, error analysis).
Take projects end-to-end from research to production deployment, monitoring, and iteration in cloud environments.
Work with AI-first development tools (e.g. Claude Code, Cursor, Codex) to accelerate development.

As our AI Team Lead, you will sit at the intersection of cutting-edge research and production engineering, guiding a high-performing team of Data Scientists and Engineers. This is a deeply hands-on leadership role where you will drive the end-to-end lifecycle-from initial research and PyTorch training to deploying sophisticated multi-modal AI (Text, Image, Audio, and Video) in high-throughput environments. Join us in building the future of responsible AI adoption and digital integrity.

Responsibilities
Manage and grow a high-performing team of AI researchers and engineers, fostering technical excellence.
Drive the design and training of multimodal models applications (Text, Image, Audio, Video) using PyTorch.
Own the AI roadmap from initial concept to scalable, high-throughput production.
Partner with Product, Backend, and Security teams to integrate AI solutions globally.
Stay at the forefront of GenAI/LLM research to continuously improve our detection engines.

Were looking for a technical professional, with proven experience for building secure, scalable artificial intelligence systems as well as data-for-AI solutions. In this role you will develop our cutting-edge AI Data platforms used by world-leading brands.
Responsibilities:
Data Pipelines: Clean, preprocess, and analyze large datasets to ensure high-quality inputs and results.
Deployment & Integration: Help deploy AI agents into production environments and integrate them into existing workflows.
Monitoring & Optimization: Evaluate model performance, test for accuracy, and tweak algorithms for continuous improvement.
Documentation: Maintain clear documentation for AI model architectures, data pipelines, and deployment processes
Scale: Build highly scalable solutions that can leverage large data sets effectively

The mission:

Take this from concept to a category-defining business. Zero to outcomes. You will:

Live in the market until you understand it better than anyone in our company.

Define the strategy, the wedge, the offering, the pricing, and the brand.

Build the early product and the early GTM in parallel.

Land the first paying customers yourself.

Build the team you need to scale once we have signal.

Think of it as founding a startup with us as your first investor, your distribution advantage, and your operational backbone, rather than as a corporate initiative.

What you'll own end to end:

Market. Talk to 50+ AI Implementers in your first 90 days. Map the segments (technical builders vs business strategists, project-based vs retainer, generalist vs vertical specialists). Map the communities, the influencers. Decide which segment we go after first and why.

Strategy. Build the business case, the product thesis and the GTM foundations. What's the offering? What's the wedge? What's the pricing model? What does the unit economics look like at scale? What are the four to five things we have to be opinionated about, and what are we deliberately not doing?

GTM. Design the acquisition motion from scratch. Community, content, partnerships, paid, sales-led, product-led, some combination. Test cheap, kill what doesn't work, double down on what does. Treat GTM as foundational, not as something we figure out after product is built.

Product. Partner with engineering and design to ship the first version. You won't build it alone, but you'll set the bar for what it is, what it isn't, and what good looks like. Customers. Land the first 10 paying customers personally. Get on the calls. Read every email. Watch every demo. Be the most informed person on what's working and what isn't.

This is a role for someone who wants to build the org of the future, where agents and people work side by side, and is ready to own the program that gets a company there! Youre taking over the leadership of our AI Transformation: the internal AI work across every function. Youll own the program end-to-end.

What Youll Do?
Run the three pillars (Adoption, Infrastructure, Builds) and the foundation that ties them together. Adjust as we learn, and make it real on the ground.
Lead the change, not just the tooling - the hard part isnt building the agent, its getting people to change how they worked for years. Youll drive that change, build trust with VPs, and push where it matters.
Manage and grow the team - both direct reports and dotted line champions. Youll grow the team from there as the program scales.
Run the prioritization machine - choose whats the right effort, from vertical low hanging fruit and all the way to horizontal initiatives.
Make agents as employees real - every agent has a manager, a cost, a performance review, and a forward plan. Managers are measured and rewarded on how their agents perform.
Measure and report impact - hours saved, adoption, leverage and metrics you define and instrument. Report up. Adjust the program based on what the numbers say.
Stay hands-on - this isnt a role for someone who only writes plans. You build agents and workflows yourself when it makes sense, sit in proposal reviews with the PM, weigh in on architecture with the Engineer, and ship things when needed.

Location: Tel Aviv. Hybrid.

Required Senior AI Infrastructure Engineer
As a Senior AI Infrastructure Engineer, you'll lead the development and scaling of our machine learning training and AI infrastructure, enabling our AI and Research teams to build, train, and deploy cutting-edge AI solutions at scale. You'll collaborate across research, engineering, and business teams to create the foundations that power our AI innovation.
Youll Own:
Developing and evolving our machine learning platform, including shared libraries, development standards, CI/CD pipelines, and ML/AI tooling.
Designing and building scalable infrastructure that supports large-scale model training, experimentation with classic models and LLM, and validation.
Driving MLOps strategy and best practices, partnering with cross-functional teams to improve developer productivity, platform reliability, and operational efficiency.
Youll Solve:
Scaling machine learning infrastructure to support growing AI workloads, increasingly complex models, and rapid experimentation cycles.
Creating standardized, efficient workflows that enable research and engineering teams to move from experimentation to production faster and more reliably.
Youll Impact:
Accelerate AI innovation by building robust infrastructure that enables teams to develop, train, evaluate and validate models efficiently.
Improve the scalability, reliability, and consistency of machine learning development across our AI organization.

Required Senior AI-Native Software Engineer
Role summary:
You will join a high-impact AI engineering team within R&D, owning problems end-to-end from understanding the business need, through architecture and implementation, to production monitoring. This is a new way of working: you'll work directly with business stakeholders, ship AI-driven capabilities at speed, and help define the methodology as we build it.
As a member of the AI Foundations organization, you will lead best practices, champion early adoption of new technologies, and influence the direction of our R&D guild - building and shipping cutting-edge agentic applications that deliver seamless experiences to our users.
Location:
Hybrid - Herzliya, Israel
Full-time
What you'll do:
Own the full development loop understand the business problem, define the solution, architect it, build it, ship it, and monitor it in production
Use AI as your primary development tool, achieving in a day what used to take a team a week
Collaborate with your team and business stakeholders to define decision logic, risk thresholds, and success metrics
Design and build evaluation frameworks as part of every solution you don't ship what you can't measure
Own production readiness monitoring, alerting, and observability go in on day one
Contribute to shaping team practices, tooling, and engineering standards across the pod
Decompose business problems into agentic workflows, orchestrated flows, and reusable capabilities.