לוח משרות דרושים AI Engineer בתל אביב יפו, לחיפוש זה נמצאו 98 משרות

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

As an AI Engineer at Glow, you will own the intelligent decision-making pipelines that turn complex workspace telemetry into autonomous security actions. You will design, build, and deploy the autonomous reasoning workflows and advanced data classification systems that drive Glow's preventive operating model. Your focus will be on creating resilient, production-grade AI systems capable of deep policy comprehension, real-time prevention at the point of adoption, and autonomous remediation of existing risks.

This is a ground-floor opportunity to shape the AI strategy of a fast-growing cybersecurity company alongside a lean, elite team of builders.

WHAT YOULL DO

End-to-End Ownership: Own our AI capabilities entirely from initial research, architectural design, and prototyping, through to production deployment, optimization, and continuous monitoring.

Design & Build Agentic Workflows: Architect multi-step AI agents capable of autonomously investigating workspace risks, interpreting complex enterprise policies, and taking precise remediation actions.

Integrate Multi-Faceted ML: Bring innovation and creative thinking to our core engine. Implement diverse ML models across our entire research and product pipeline-utilizing clustering, text extraction, document analysis, and tabular data classification.

Ship Production-Grade AI: Build high-throughput, resilient, and fault-tolerant production code. You will ensure our AI pipelines and agentic workflows are highly predictable, deeply observable, and built to scale under enterprise-grade loads.

Implement Guardrails & Evaluation: Build continuous evaluation frameworks to benchmark agent accuracy, mitigate hallucinations, and enforce strict data security/privacy guardrails.

We're building a new orchestration platform for AI datacenters-software that turns a complex, multi-vendor stack of compute, networking, and accelerators into something customers can deploy, operate, and optimize with confidence.
You'll join a small, growing engineering team where every engineer ships. In this hands-on development role, you'll build backend services, contribute to system design, and own features from implementation through production, working closely with experienced engineers.
If you enjoy solving challenging engineering problems, building new products, and seeing your work run at scale in real AI datacenters, this role is for you.
What You'll Do
- Build and maintain core services and features for an orchestration platform spanning compute, networking, and accelerators.
- Translate customer requirements into APIs, workflows, and data models in collaboration with the team.
- Deliver features end-to-end, from prototyping and implementation to testing, deployment, and production support.
- Write reliable, maintainable code and contribute to shared libraries, automated tests, and CI/CD pipelines.
- Contribute to design discussions and make practical implementation tradeoffs around performance, reliability, and simplicity.
- Participate in code reviews, share knowledge, and help improve engineering practices.
- Troubleshoot issues and improve service performance, observability, and operational readiness.

Job responsibilities
Design, build, and maintain backend infrastructure and microservices supporting AI-driven features.
Collaborate closely with the infra and data groups to ensure smooth integration of AI models and services into Bobs core systems.
Develop and optimize APIs, data pipelines, and background processing workflows.
Ensure performance, scalability, and resilience of production systems handling real-time AI interactions.
Drive engineering excellence through code reviews, architecture discussions, and automation.
Mentor other backend developers and promote best practices in software design, testing, and deployment.

We are looking for a Senior AI Ops Engineer to join our AIOps team and help build the AI-powered platforms, agents, and workflows that enable engineering teams across our company.
This is a hands-on role for someone who can take AI solutions from idea to production, build the infrastructure around them, and create the observability needed to understand and improve how they behave in the real world.
Job responsibilities
Build and operate production-grade AI agents, AI-enabled tools, and workflows that empower developers and operational teams.
Design agentic systems using frameworks such as LangGraph, LangChain, and Supervisor, including RAG, MCP integrations, tool calling, and guardrails.
Build and evolve an AI SDLC harness that helps teams plan, implement, validate, govern, and safely deliver AI-assisted software changes.
Lead AI observability with Langfuse and OpenTelemetry, including tracing, evaluations, quality measurement, and cost analysis.
Build automated evaluation frameworks, including LLM-as-a-judge, to improve the reliability and quality of AI systems.
Design and maintain the infrastructure, CI/CD pipelines, GitOps workflows, and security controls that support AI services.
Develop internal developer tooling and automation that removes friction across the engineering organization.
Partner with engineering and product teams to turn real operational challenges into scalable AI-powered solutions.

as a member of the hardware infrastructure eda compute team, you will optimize, scale, and support workload scheduling systems that directly impact design velocity and infrastructure efficiency. you will combine deep scheduler expertise (lsf, slurm) with automation, ci/cd, and llm-based tooling to keep the eda and gpu-accelerated ai compute environment resilient and measurable.
what you'll be doing:
manage, scale, and optimize job scheduling systems (lsf, slurm, etc.) in a large-scale, multi-site environment supporting eda and other compute-intensive workloads
analyze scheduler and infrastructure performance data to identify systemic bottlenecks and drive measurable improvements in utilization, throughput, and turnaround time
building and improving our ecosystem around gpu-accelerated computing
define and develop automation scripts and wrappers tools for ic design environment
identify recurring operational challenges and implement targeted automation or process improvements to reduce manual effort and prevent repeat incidents
root cause analysis and suggest corrective action for problems large and small scales
partner directly with customer teams to clarify requirements, translate technical tradeoffs, and drive issues to closure

we have been defining computer graphics, pc gaming, and accelerated computing for more than 25 years. with an outstanding legacy of innovation, driven by phenomenal technology and extraordinary people, we are looking for a distinguished engineer to help shape the future of ai infrastructure. as part of our advanced development and research efforts, you will drive technical vision and innovation across the ai software stack, spanning gpu computing, rdma, nvlink, Storage, and large-scale distributed systems. you will research next-generation system designs, influence product and platform direction, and help define how massive-scale ai environments are built, optimized, and operated through hardware-software co-design.
what you'll be doing:
research, define, and drive co-design solutions across ai software stacks involving gpu computing, rdma networking, nvlink interconnects, Storage, and large-scale distributed infrastructure
lead deep technical investigations and proof-of-concept development in areas related to distributed ai, deep learning systems, high-performance computing, virtualization, memory management, and large-scale systems design
work closely with multiple teams across the company to advance next-generation ai infrastructure technologies and influence cross-stack co-design decisions
analyze end-to-end system bottlenecks and opportunities across compute, networking, memory, and Storage layers to improve performance, scalability, and efficiency
guide the design of environments that support massive-scale ai training and inference workloads in production
 we want to hear from you!
we are committed to fostering a diverse work environment and proud to be an equal opportunity employer. as we highly value diversity in our current and future employees, we do not discriminate, including in our hiring and promotion practices, on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status, or any other characteristic protected by law.

We're looking for an AI-native engineer to build the autonomous agents that power our SalesOS. You'll work directly under the engineering head and the founders, and you'll be the one bringing the agents to life. The architecture gets set above you; the agents get built by you. Do this well and you grow fast.
We're now bringing in the engineer who will turn agent designs into shipped, reliable systems - the discovery prompts, the objection-handling logic, the orchestration that decides what the coach says and when.
This role sits close to the heart of the product. The person who gets this right will write code that real reps lean on during real deals.
A word of honesty:
This is an early-team role at a fast-moving company, which means it is hands-on and unglamorous in the way that matters. You will write the agent code, not just diagram it. You will build the eval harness yourself. You will chase down why a coaching response was wrong at 9pm because a customer demo is tomorrow. You will work from a product requirement, figure out the right agent design, and live with the calls you make. The senior team sets the architecture, but inside it you own the implementation end to end - and "it works on my machine" is not the bar. If you want a research seat where you read papers and prototype in notebooks, this is the wrong role. If you want to ship autonomous agents into production and watch them earn their keep on live sales calls, read on.
What you'll do:
Build and iterate the agents themselves - from design to production: coaching reps, analyzing deals, surfacing insight in real time.
Build tool-use and integrations that let agents reach CRMs, transcripts, calendars, and internal data (Gong, Fireflies, Stripe, Apollo, Resend).
Build prompt pipelines for structured outputs, classification, summarization, and coaching feedback - and the eval harnesses that prove they're good.
Write orchestration code that manages agent state, retries, fallbacks, and human-in-the-loop checkpoints - including the failure paths most people skip.
Own the database operations, FastAPI endpoints, and background workers that the agent workflows run on.

As an AI Engineer, you'll work on cutting-edge technologies, tackle challenging technical problems, and influence the engineering culture at Base44. You'll become an industry leader in AI-native development while improving your skills and collaborating with top-notch engineers. You'll be part of a team that lives and breathes AI, redefining how software is built from the ground up.
In your day-to-day, you will:
Work on cutting-edge technologies and become an industry leader
Use AI to bridge ideation to production in record time without compromising on code quality, security, or architecture.
Improve your skills and learn from top-notch engineers
Tackle tough problems and find creative ways to solve them
Push your code to millions of users every day
Solve tough technical challenges on a cutting-edge, AI-powered platform
Influence our engineering culture by contributing to internal knowledge sharing and, if you're interested, the open-source community.

Join our marketing engineering team and take ownership of the AI-powered systems, internal tools, and automations that multiply our team's impact.
In this role, you will bridge the gap between engineering and marketing operations by building fully autonomous agents, streaming data pipelines, and internal AI tools. Your daily work will involve identifying manual marketing workflows, designing system architectures, and shipping production-ready software end-to-end to scale our marketing infrastructure. As our AI Marketing Builder you will:
Design, build, and deploy agentic AI workflows and multi-step autonomous pipelines to automate complex marketing operations
Develop AI-powered internal tools, including content assistants and automated campaign generators, leveraging our own product ecosystem
Build and maintain scalable data pipelines pulling real-time performance analytics from various marketing channels into unified dashboard platforms
Architect end-to-end full-stack systems from scratch, choosing the optimal technical stack, APIs, databases, and server infrastructure
Collaborate directly with non-technical marketing stakeholders to translate operational challenges into clean technical solutions
Take full ownership of the product roadmap, surfacing technical blockers early and shipping high-impact software continuously
Work closely with the R&D team on marketing related feature launches.

We're looking for an Experienced AI Product Marketing Manager someone who lives and breathes AI and wants to channel that into marketing. Think of this as the growth hacker of 2026, you're AI-native first, marketer second. AI tools are your default way of working, You build with Claude Code or OpenClaw instead of spreadsheets and slide decks. If you've ever built an AI agent for fun, automated a workflow because you were bored, or shipped a side project using vibe coding - and you're excited about applying that energy to marketing - this role is for you.
In your day-to-day, you will:
Own marketing projects end-to-end - from strategy to execution - using AI as your primary working environment, not just an assistant
Create campaigns, landing pages, product content, and customer stories at a speed and volume that would be impossible without AI fluency
Build your own tools, automations, and AI workflows to solve marketing problems
Run growth experiments across channels, rapidly testing messaging, creatives, and audiences to find what drives sign-ups and engagement
Lead product launches alongside Product and Growth teams - shaping positioning, messaging, and go-to-market plans
Stay on the bleeding edge of AI capabilities, constantly scouting new models and tools and figuring out how to apply them to marketing before anyone else does.

You'll be a senior engineer on a deliberately flat, entrepreneurial team. Day to day you'll design and build agentic AI systems - agents, workflows, the orchestration layer that ties them together, and the infrastructure that makes all of it reliable at scale. This is hands-on building, not oversight.
What you'll do
Design and build agentic workflows and the AI systems behind them - agents, the orchestration layer, and the infrastructure that runs them reliably
Take significant, ambiguous areas of the system and drive them from rough idea to working capability
Help scale the platform from a first working workflow to something used across the marketing organization
Contribute to evaluation, quality, and reliability - making sure systems don't just run, but run well
Work directly with the people who'll use what we build, and translate their needs into systems.

We're hiring an exceptional Applied AI Scientist to join our team!
In this role, you'll design and deploy enterprise-grade AI agents that drive real business impact across our core functions - Sales, Marketing, Customer Success, Customer Support, Risk, and Finance. You'll be building agentic systems that execute complex, multi-step business workflows end-to-end. The ideal candidate is a hands-on builder with deep AI expertise who thrives on turning complex business problems into reliable, production-grade AI systems.
Why join us?
We are the AI-powered platform for finance automation, elevating how finance teams operate in the global economy. We empower our customers to scale faster and smarter by removing the complexities of doing global business and accelerating their finance operations efficiency. Our platform provides a comprehensive suite of finance automation solutions designed for mid-market businesses across accounts payable, global payouts, procurement, employee expenses, corporate cards, supplier management, tax compliance, and treasury. Our partners with leading financial institutions such as Citi, Wells Fargo, J.P. Morgan, and Visa, enabling over 5,000 global companies to efficiently and securely pay millions of suppliers and payees across 200+ countries and territories, in 120 currencies.
In This Role, You Will Be Responsible For:
Enterprise AI Agent Development: Designing, building, and deploying AI agents that automate and augment high-value workflows across business functions - including lead qualification and outreach (Sales & Marketing), churn reduction(Customer Success), and Ticket deflection (Support).
Agentic Architecture & Orchestration: Owning the end-to-end agent stack - from tool use, memory management, and multi-step planning to human-in-the-loop escalation patterns, guardrails, and audit trails suited for an enterprise fintech environment.
Knowledge Systems: Building and maintaining RAG pipelines that ground agents in our proprietary knowledge - product documentation, customer data, financial records, and internal playbooks - using vector databases, hybrid search, and re-ranking.
Evaluation & Continuous Improvement: Defining agent evaluation frameworks that measure task completion, accuracy, hallucination rates, latency, and business impact - then iterating on agent behavior based on real usage data and stakeholder feedback.
Cross-Functional Collaboration: Partnering with stakeholders across the company to define agentic use cases, translate business requirements into technical features, and deliver products that create measurable ROI - from data preprocessing through product deployment.

We're looking for a hands-on Agentic AI Engineer to build practical, high-impact AI solutions for our Product Support organization.
This is not a research role. You'll design and ship real AI systems that improve support workflows and processes - building custom solutions where off-the-shelf tools fall short. The role also requires close collaboration across teams: aligning with different stakeholders' needs and priorities, and building strong, ongoing partnerships across the organization.
Responsibilities:
Partner directly with business teams to identify automation and optimization opportunities
Design and implement agent-based AI workflows to automate internal processes end-to-end
Design and build LLM-powered tools (agents, workflows, copilots) using frameworks like LangGraph / LangChain
Develop RAG pipelines, integrate multiple data sources, and build intelligent automation flows
Deep-dive into company data - validate quality, uncover gaps, and ensure AI solutions are built on solid foundations
Take solutions from idea → prototype → production
Governance, Reliability & Security
Ensure AI workflows comply with security, privacy, and compliance requirements
Implement guardrails, approvals, logging, and human-in-the-loop mechanisms where needed
Monitor AI performance, errors, hallucinations, and drift
Collaboration & Enablement:
Partner with business owners to identify automation opportunities
Translate business requirements into AI-driven solutions
Document AI flows, decision logic, and operational runbooks
Educate internal teams on AI capabilities and limitations.

we are seeking a software engineer with a passion, knowledge, and experience for taming, training, and controlling LLM and ML models, for diverse use cases and users. In this role, you will take part in all the steps of shipping GenAI, from idea to enterprise-grade solution, across all stages of the product development lifecycle.

Responsibilities:
Get deeply acquainted with a problem domain, investigate and prototype technologies and architectures for an adequate solution
Collaborate with product and engineering teams to translate customer-facing features into detailed product specifications and solution design
Build GenAI-powered applications/agents from the ground up - from data collection and model training to deployment and production monitoring
Conduct experiments and model evaluations, analyze performance metrics, and iterate rapidly to maximize accuracy and effectiveness
Develop and propagate best practices, including reusable tools or frameworks, to ensure robust and maintainable GenAI engineering
Stay updated with emerging trends and advancements in AI - including new models, frameworks, and techniques - and bring forward innovative ideas for their adoption