לוח משרות דרושים AI Engineer עבודה היברידית

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.

Required AI / Computer Vision Algorithm Engineer
Key Responsibilities:
AI/Computer Vision Engineer to develop image-processing algorithms for next-generation ophthalmic diagnostic devices.
Full time, Hybrid
Develop advanced computer-vision algorithms for medical imaging.
Detect biomarkers related to specific use cases
Build models supporting diagnostic decisions.
Train deep-learning models (CNNs, ViT, Autoencoders).
Work with Python, PyTorch/TensorFlow, OpenCV.

An AI startup is looking for an AI Engineer to join its core team at an advanced and critical pre-launch stage.
This role focuses on building AI capabilities at the heart of the product, working on LLM-based systems and AI agents, and collaborating closely with product and data teams.
Responsibilites:
Develop and implement AI agents and LLM workflows
Work on prompting, retrieval, context management, and evaluation pipelines
Bring AI-based features and modules into production
Improve system answer quality and reliability
Collaborate with the data team on semantic layers and data modeling
Develop in Python within a production environment
Work with Docker, Git, and modern development workflows

An AI startup is looking for an AI Engineer to join its core team at an advanced and critical pre-launch stage.
This role focuses on building AI capabilities at the heart of the product, working on LLM-based systems and AI agents, and collaborating closely with product and data teams.
Responsibilities:
Develop and implement AI agents and LLM workflows
Work on prompting, retrieval, context management, and evaluation pipelines
Bring AI-based features and modules into production
Improve system answer quality and reliability
Collaborate with the data team on semantic layers and data modeling
Develop in Python within a production environment
Work with Docker, Git, and modern development workflows

You are a Senior AI Software Engineer looking to join a GenAI Infrastructure team and build production-grade GenAI capabilities and the infrastructure behind them. You will be a hands-on engineer focused on AI agents, RAG pipelines, tool-calling workflows, backend services, data access layers, evaluation, observability, and reusable GenAI infrastructure used across product teams. You will not be focused solely on model research or prompt design. Instead, you will be a strong software engineer who can design, build, ship, and operate reliable AI-powered systems in production.
A Day In Your Life
Build reusable infrastructure, SDKs, and internal frameworks for AI-powered product capabilities.
Design and implement AI agents, RAG flows, tool-calling workflows, and LLM orchestration pipelines.
Build production-grade GenAI services, APIs, and backend infrastructure.
Integrate LLM workflows with internal microservices, data platforms, vector search, and event-driven systems.
Design secure data-access patterns that enforce authorization, tenant separation, and user-level scope.
Implement evaluation, tracing, monitoring, and quality-control mechanisms for GenAI systems.
Improve latency, reliability, fallback behavior, cost efficiency, and production readiness.
Work on customer-facing AI experiences, including conversational and proactive agentic product flows.
Collaborate with backend engineers, product managers, data engineers, AI/ML engineers, and domain experts.

Required Al Infrastructure & Reliability Engineer
What this role is really about
Youll join a 3-person platform team within our Business Technology group -owning the internal infrastructure that our AI platform and its users depend on. This isnt a product engineering role, and it isnt ticket work or babysitting pipelines someone else built. Youre building and operating the internal foundation that the company runs on. The work covers the full stack of platform engineering: core cloud infrastructure (AWS, Kubernetes, IaC), CI/CD pipelines, AI-driven infrastructure components, and the SRE and observability practice that keeps it all honest -metrics, alerting, incident response, and reliability standards. As our AI capabilities grow, so does the complexity underneath them, and staying ahead of that is central to the role. If you treat infrastructure as a product -reusable, automated, observable, and built to last -this is your kind of role.
Job responsibilities
DevOps & AI-Driven Infrastructure - own CI/CD, deployment processes, and release reliability. Build and operate cloud infrastructure that is automated, intelligent, and continuously self-improving - not just managed.
Design and build our Terraform repository and IaC pipeline from scratch -AI-assisted generation, drift detection, and policy enforcement built in.
Build AI-driven GitHub Actions pipelines -automated code review, risk assessment, and intelligent deployment decisions.
Manage Kubernetes workloads across AWS accounts -zero downtime, fully automated, nothing left behind.
Embed AI into the operational layer -proactive drift detection, automated remediation, and intelligent scaling toward a self-healing runtime.
Reliability & SRE -improve uptime, resilience, and incident response.
Define and enforce SLOs/SLIs, error budgets, and on-call practices.
Lead incident response, postmortems, and systemic reliability improvements.
Own AI-specific reliability: model latency SLOs, token quota monitoring, rate limit handling, fallback and retry strategies, and cost-per-request alerting.
Observability & Telemetry - increase visibility, reduce noise, improve troubleshooting.
Establish and continuously evolve the observability stack: metrics, logs, distributed tracing, and alerting tuned for both application and AI workloads.
AI / LLM Operations- bringing AI systems to production and operating them at scale, with a focus on reliability, performance, and trust.
Own the AI infrastructure layer: rate limits, quota management, latency SLOs, and fallback strategies (retries, circuit breakers).
Operate LLM APIs in production with resilience and cost attribution per team/model.

we are looking for a AI Adoption Engineer.
Responsibilities
1. Use Case Discovery & Prioritization
Embed with non-R&D teams (CS, Sales, Marketing, Finance, HR, Operations) to understand workflows and identify where AI creates the highest leverage.
Define and prioritize whats worth building, maintaining a live backlog aligned with VP of AI.
Good discovery is the difference between automation that gets used and automation that gets ignored.
2. AI Tool Development & Delivery (~40%)
Build custom tools, workflow automations, Cowork skills, and AI integrations with existing business systems.
Validate with real users, iterate on adoption signals, and transition stable tools to team ownership.
This role leads by shipping. Working tools - not proposals - are the primary output.
3. Enablement & Adoption
Ensure tools get used: onboarding sessions, playbooks, and hands-on support through behavior change.
Build team confidence and self-sufficiency over time; avoid creating dependency on a single person.
4. AI Landscape Awareness
Evaluate new models, tools, and capabilities for internal relevance and bring what matters back to .
Collaborate with VP of AI to maintain a current roadmap for internal AI operations.

we are looking for a AI Architect.
While team leads own delivery and execution, the organization is missing a senior, hands-on technical leader whose role is to:
Provide system-level AI architectural guidance
Drive hands-on applied innovation
Establish AI best practices and compliance readiness
Serve as a senior technical sounding board across teams
This role fills that gap.
It is a director-level individual contributor role, designed to bring deep technical leadership, fast execution, and architectural clarity

We are looking for an AI Engineer to play a central role in building, evaluating, and advancing our AI models. You will own and evolve our benchmarking and evaluation capabilities for foundation models and multimodal systems, while also working closely with modeling teams to support model development, iteration, and validation. This role sits at the intersection of software engineering, model understanding, and applied AI, with broad influence on how models are built, compared, and improved across the organization.

Location: Ramat Gan (hybrid model).

What will you do?
Own & Evolve Benchmarking - Design, build, and maintain our benchmarking suite for foundation models and multimodal AI systems.
Define Core Abstractions - Create clean, extensible abstractions and APIs for datasets, tasks, models, metrics, and evaluation workflows.
Develop Metrics & Evaluations - Implement metrics that capture predictive performance, biological relevance, and multimodal alignment.
Support Model Development - Work closely with AI scientists and data scientists to integrate new models, tweak architectures, and enable rapid, fair iteration.
Bring in New Models & Baselines - Add external and internal models to benchmarks and ensure meaningful comparisons.
Explore Data When Needed - Dive into data and results to debug evaluations, understand model behavior, and unblock modeling work.
Enable Rigor & Reproducibility - Ensure evaluations are consistent, well-versioned, and trustworthy over time.

We are seeking an AI Engineer to design, build, and deploy AI-powered capabilities within our product.
This role focuses on integrating machine learning models and large language models (LLMs) into scalable software systems and delivering reliable AI-driven features to production.
The AI Engineer works at the intersection of software engineering, AI systems, and infrastructure.
transforming AI technologies into practical applications.
Responsibilities:
Build applications powered by machine learning and large language models (LLMs).
Implement capabilities such as intelligent assistants, semantic search, automation, and recommendation systems.
Integrate AI functionality into backend services and product workflows.
Design and implement retrieval pipelines, embedding pipelines, and inference workflows.
Build Retrieval-Augmented Generation (RAG) systems and AI-driven services.
Create scalable AI architectures capable of handling production workloads.
Package and deploy AI models as production services.
Optimize inference performance, scalability, and latency.
Monitor AI services to ensure reliability and performance.
Develop backend services and APIs that expose AI capabilities.
Integrate AI systems with databases, internal services, and external APIs.
Contribute to system architecture and microservices design.
Implement logging, metrics, and observability for AI systems.
Track model performance and system reliability in production environments.
Work closely with product managers, engineers, and data scientists.