לוח משרות דרושים AI Engineer משרה מלאה, לחיפוש זה נמצאו 138 משרות

we are looking for an AI Specialist with deep, hands-on expertise in modern AI platforms to train, prototype, build and ship AI-powered solutions across the organization. Working closely with business stakeholders and IT peers , this role is a combination of an ability to design and work with AI consumers across the organization and technical - to turn the business needs into working AI solutions at speed using the latest agentic and low-code AI tools.
In this role you will also nurture the AI builder's community with advanced knowledge, technical sessions, builders CI/CD process support, creating business data foundations and literacy and responsible AI training and practices
Responsibilities:
Rapidly prototype and deliver working proof-of-concepts and working builds that translate business requirements into functional solutions, iterating quickly toward production.
Lead advanced, in-depth technical sessions, workshops, and hands-on labs for technical and semi-technical audiences, building internal capability in AI-assisted development.
Build and optimize agentic workflows, tool integrations, to maximize solution capability and reliability.
Establish and promote best practices for AI-assisted development, code quality, reusability, and responsible build standards across teams.
Drive build projects from concept through delivery, ensuring quality, performance, and maintainability.
Collaborate with Business Applications, IT R&D, Analytics and business units to identify high-impact opportunities and deliver measurable value through custom-built AI solutions.
This includes identification of required foundations to support the builders community (literacy, business models, RBAC foundations etc.)
Stay continuously current with the fast-moving AI development landscape - new models, platforms, agent frameworks, and tooling - and bring that knowledge into the organization.
Ensure compliance with corporate information security standards, data handling, and governance policies.

We are building the next generation of AI-powered investigator tools . Our cloud-based AI platform delivers cutting-edge capabilities including multimodal analysis, embeddings, OCR, speech-to-text, face detection, image and text classification, and LLM completions through a scalable serverless architecture.
As a Senior Software Engineer on the AI Platform team, you will be part of the core team that designs, builds, maintains, and evolves the shared AI service platform that powers real investigative workflows at enterprise scale. You will help turn advanced AI concepts into production-grade serverless systems, shaping orchestration, retrieval, context, and backend architecture across the platform.

As AI Quality Architect, you are the person who makes quality native to the agentic SDLC. You design the systems, standards, and intelligence layers that ensure every stage of an AI-accelerated pipeline - from requirement ingestion to autonomous deployment - is observable, trustworthy, and continuously improving. You don't retrofit testing onto AI workflows; you architect quality into them from the ground up.
How will you make an impact?
Agentic Quality Architecture
Design the end-to-end quality architecture for agentic SDLC pipelines - spanning requirement analysis, code generation, test creation, execution, triage, and release gates
Define how quality agents are orchestrated: which decisions they own autonomously, which require human-in-the-loop checkpoints, and how confidence thresholds govern both
Architect multi-agent quality workflows: requirement validation agents, test generation agents, failure triage agents, and regression analysis agents working in coordinated pipelines
Establish trust and verification models for agent-produced artifacts - test code, assertions, coverage reports, and defect analyses must all be auditable and traceable
Own the architectural patterns for quality feedback loops between agents: how a deployment agent learns from a triage agent's findings, and how that signal improves future generation
AI-Native Test Engineering Platform
Design and own the LLM-powered test generation platform - from natural language requirement ingestion to executable, maintainable test output
Architect the evaluation harness that continuously measures test generation quality: coverage delta, false-positive rates, assertion accuracy, and maintenance burden over time
Build the self-healing test infrastructure layer - agents that detect broken selectors, drifted APIs, or changed behaviors and propose or apply fixes autonomously
Define the prompt engineering standards, context injection patterns, and RAG architectures that ground test generation agents in real codebase context
Architect test artifact governance: versioning, ownership attribution (human vs. agent), rollback capability, and confidence scoring for every generated artifact
Quality Gates in Autonomous Pipelines
Design intelligent, adaptive quality gates that operate at the speed of agentic CI/CD - gates that reason about risk, not just pass/fail thresholds
Build risk-scoring models that dynamically adjust gate strictness based on change scope, code origin (human vs. AI-generated), historical failure patterns, and deployment context
Architect the observability layer for agentic pipelines: what signals indicate a pipeline agent is making poor quality decisions, and how are those signals surfaced in real time
Define the integration patterns between quality gates and orchestration platforms (LangChain, LlamaIndex, custom agent frameworks) used across the engineering org
Establish rollback and circuit-breaker patterns for autonomous deployments triggered by quality signal degradation.

As AI Quality Architect, you are the person who makes quality native to the agentic SDLC. You design the systems, standards, and intelligence layers that ensure every stage of an AI-accelerated pipeline - from requirement ingestion to autonomous deployment - is observable, trustworthy, and continuously improving. You don't retrofit testing onto AI workflows; you architect quality into them from the ground up.
How will you make an impact?
Agentic Quality Architecture
Design the end-to-end quality architecture for agentic SDLC pipelines - spanning requirement analysis, code generation, test creation, execution, triage, and release gates
Define how quality agents are orchestrated: which decisions they own autonomously, which require human-in-the-loop checkpoints, and how confidence thresholds govern both
Architect multi-agent quality workflows: requirement validation agents, test generation agents, failure triage agents, and regression analysis agents working in coordinated pipelines
Establish trust and verification models for agent-produced artifacts - test code, assertions, coverage reports, and defect analyses must all be auditable and traceable
Own the architectural patterns for quality feedback loops between agents: how a deployment agent learns from a triage agent's findings, and how that signal improves future generation
AI-Native Test Engineering Platform
Design and own the LLM-powered test generation platform - from natural language requirement ingestion to executable, maintainable test output
Architect the evaluation harness that continuously measures test generation quality: coverage delta, false-positive rates, assertion accuracy, and maintenance burden over time
Build the self-healing test infrastructure layer - agents that detect broken selectors, drifted APIs, or changed behaviors and propose or apply fixes autonomously
Define the prompt engineering standards, context injection patterns, and RAG architectures that ground test generation agents in real codebase context
Architect test artifact governance: versioning, ownership attribution (human vs. agent), rollback capability, and confidence scoring for every generated artifact
Quality Gates in Autonomous Pipelines
Design intelligent, adaptive quality gates that operate at the speed of agentic CI/CD - gates that reason about risk, not just pass/fail thresholds
Build risk-scoring models that dynamically adjust gate strictness based on change scope, code origin (human vs. AI-generated), historical failure patterns, and deployment context
Architect the observability layer for agentic pipelines: what signals indicate a pipeline agent is making poor quality decisions, and how are those signals surfaced in real time
Define the integration patterns between quality gates and orchestration platforms (LangChain, LlamaIndex, custom agent frameworks) used across the engineering org
Establish rollback and circuit-breaker patterns for autonomous deployments triggered by quality signal degradation.

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.

We are looking for a visionary AI Lead to build our internal AI platform and architect adaptive intelligence systems that serve as a dedicated security architect for each of our customers. You will lead a team of AI Researchers and Engineers to move beyond simple integrations and build true autonomous security solutions.
What You Will Build:
Autonomous Workflows: Build agents that execute security workflows end-to-end, automating detection, investigation, and response processes.
Runtime Defense: Productize research into active runtime guardrails and defensive control mechanisms.
Internal AI Platform: Build the shared infrastructure for models, training, and evaluations to enable safe, scalable AI features across the organization.
Adaptive Intelligence: Oversee the development of models that learn from customer-specific environments to generate high-signal, personalized insights.
Responsibilities:
Lead the AI Research Group, managing AI Researchers, Engineers, and DevOps.
Own the intelligence engine end-to-end, delivering next-generation analytics and contextual visualization features.
Drive the strategic "build vs. partner vs. buy" decisions for cloud prevention and AI controls.
Collaborate with the Threat Team to develop AI-driven threat detections against emerging attack vectors and novel AI threats.

We are looking for a hands-on AI Engineer to help build our core intelligence engine and infrastructure. In this role, you will work on exploring and building autonomous solutions that execute security workflows end-to-end, moving capabilities from research concepts to scalable production features.
What You Will Build:
Internal AI Infrastructure: Contribute to a shared internal AI platform for models, data, training, and observability.
Autonomous Capabilities: Build solutions that automate complex security operations and investigative workflows.
Active Defense Mechanisms: Help productize runtime guardrails into customer-facing protective controls and monitoring systems.
Responsibilities:
Develop and implement the engineering backend for AI-driven security features.
Build and maintain the internal mechanisms that allow for safe and scalable AI feature deployment.
Collaborate with AI Researchers to translate findings into production-grade autonomous workflows.
Implement runtime guardrails and AI-focused data security capabilities.

Were looking for a hands-on AI Engineer
to build and own the agentic workflows at the heart of our security intelligence engine. This is a
backend-focused engineering role
: youll design, build, and maintain the autonomous systems that execute security workflows end-to-end - taking capabilities from research prototype to scalable, production-grade infrastructure.
Youll be working at the edge of applied AI, turning LLMs and agentic frameworks into reliable, observable systems that operate in real customer environments.
What Youll Build:
Agentic Workflows (Core Focus): Design and maintain the orchestration backbone for multi-step, autonomous agents that investigate, reason about, and act on complex security operations.
Internal AI Infrastructure: Contribute to a shared platform for models, data pipelines, training, evaluation, and observability that the whole AI team builds on.
Responsibilities:
Build and maintain the agentic workflow engine - orchestration, tool use, state management, retries, and evaluation - that powers our AI-driven security features.
Develop the backend services and APIs that deploy AI capabilities safely and at scale.
Collaborate with AI Researchers to translate findings into production-grade autonomous workflows.
Own reliability, observability, and performance of the agentic systems in production.

we are building the technical layer that brings AI-native code quality into real engineering workflows. As our partnership ecosystem grows, were looking for a principal-level engineer to turn strategic integrations into shipped products.
In this role, youll be a technical visionary and builder. You will design and implement the next generation of Model Context Protocol (MCP) servers, APIs, and platform integrations for key strategic partners like cloud providers, Anthropic, Cursor, and OpenAI. This is a high-leverage role where your expertise will directly shape how our company- the missing quality-focused system-plugs into the complex, autonomous, multi-agent development environments of tomorrow.
Ideal for someone who thinks deeply about developer experience, moves fast, and enjoys building technical infrastructure that scales through partnerships.
Mission: Make our companyunavoidable across the Agentic SDLC. Own the integration playbook that brings our company into every critical developer surface - IDEs, repos, CI/CD, code review, cloud platforms, and the AI agents shaping how software gets built. Build the technical layer that turns our company into the default quality, governance, and trust gate across the SDLC, translating cutting-edge research into integrations that scale, ship, and become embedded in how teams work.
Responsibilities
Wrap our companys capabilities as a public, composable surface. Turn our company Review, Aware, and Skills into clean, documented building blocks that any partners engineering team can adopt without hand-holding. Treat the public API, SDK, and MCP surface as a product - versioned, stable, well-documented, and obsessively focused on DX.
Be the engineering counterpart on partner conversations. Sit shoulder-to-shoulder with the Product Partnerships Lead in partner discussions. Translate ambiguous we want to integrate conversations into concrete technical scopes, integration paths (MCP vs. SDK vs. API), and shipping timelines. Be credible enough that a partners principal engineer takes the conversation seriously the first time.
Ship marketplace and cloud integrations. Own the technical execution behind cloud marketplace launches (AWS, Azure, GCP) - deployment artifacts, SaaS metering, private offer plumbing - so the commercial motion is never bottlenecked by engineering.
Define the integration playbook. Make the path from new partner interested to integration live repeatable. Document the patterns, build the reusable primitives, and reduce the per-partner engineering cost over time so the team can scale partnership volume without scaling headcount linearly.
Hold the DX bar across every partner-facing surface. Docs that work the first time. SDKs that dont surprise. Errors that explain themselves. Examples that run. If a partners engineer cant get to hello world

We are seeking a highly motivated AI Solutions Engineer to join a team leading the evaluation, adoption, and integration of AI-based tools into our development processes.
This role involves identifying opportunities to enhance workflows through AI, implementing internal tools that leverage AI capabilities, and collaborating with cross-functional teams to ensure seamless integration and usability.
Responsibilities:
Lead end-to-end AI initiatives from ideation to production
Design and deploy AI agents, automations, and data-driven solutions
Partner with business and engineering teams to deliver impactful use casesDrive prioritization based on business value and strategic impact
Define KPIs and monitoring to track performance, adoption, and ROI
Provide insights to leadership and lead cross-functional efforts in a matrix environment
Lead technical AI sessions, workshops, and internal enablement programs to drive adoption and upskill teams.

What you'll do

Context Lake and Agentic Layer

Build and maintain Context Lake on GitHub: the structured, machine-readable repository of messaging, brand guidelines, persona profiles, competitive positioning, and use cases that every agent draws from.
Design the architecture so that when a marketer updates a file, every downstream agent immediately produces sharper output. Repo structure, file schema, naming conventions, update workflows - all of it.
Build and maintain the Agentic Layer: the skills, orchestration files, automations, and shared memory structures that power SysAid's content production and campaign workflows.
Set and enforce the quality bar for both layers. Context files that agents can't reliably read are a production bug. Treat them that way.

What will you do?

Design and implement AI-driven automation workflows across business systems.

Build and maintain integrations using REST APIs and external services.

Develop and deploy RAG (Retrieval-Augmented Generation) pipelines.

Design and implement AI agents and multi-step automated processes.

Build MCP servers/tools or similar AI tool-integration frameworks.

Collaborate with business stakeholders to translate requirements into scalable solutions.

Monitor, evaluate, and improve AI system performance and reliability.

Ensure best practices in system design, security, and scalability.

We are seeking a talented engineer to work on our AI software stack-specifically, the low-level optimization of AI models and kernels for our unique hardware architecture.
Responsibilities
Develop our optimized AI libraries and related components in the SW stack.
Drive the performance optimization of AI models running on our unique architecture.

Our team is looking for a skilled professional to join the AI software group. In this role, you will focus on our AI kernel compilers, ensuring our hardware delivers peak optimization for sophisticated and custom AI kernels. You will work extensively with various kernel infrastructures, including Triton and CUDA, while collaborating closely with our AI graph compiler teams.
Responsibilities
Develop our AI kernel compilers and related components in the SW stack.
Drive the performance optimization of AI models running on our unique architecture.

This is your opportunity to get on the rocket ship and join a company that is building a cutting-edge enterprise network and secure cloud platform, and is on a fast track to becoming the worldwide market leader - dont miss it!
we are building a real-time AI runtime platform for security algorithms running inline across our global cloud and physical PoPs.
We are looking for an AI Platform Engineer to help build the infrastructure that powers high-throughput, low-latency AI security decisions in production.
You will work on a runtime engine that combines GPU-based models, from MMBERT-style models to LLMs, with CPU-based heuristics and security logic, optimized for scale, performance, reliability, and real-time execution. This is a versatile engineering role that spans AI runtime infrastructure, high-performance backend development, GPU inference, model lifecycle, and close collaboration with research teams to bring AI security algorithms into production.
Responsibilities
Build our companys AI security runtime platform for high-throughput, low-latency production serving.
Develop infrastructure for model serving, multi-model orchestration, and inline decision flows.
Optimize inference performance: batching, caching, streaming, GPU utilization, memory usage, and runtime acceleration.
Build backend orchestration and performance-critical services in Go.
Support the model lifecycle: registry integration, packaging, versioning, deployment, monitoring, and operational health.
Work closely with research and algorithm teams to productionize AI security models and algorithms at scale.