דרושים » תוכנה » AI Engineer

We are looking for a Senior Full-Stack Developer to join the hunt.
A bit about the position:
Major role in architecting and developing our Dev-Native Observability Platform
Developing and maintaining both backend services and frontend applications
Taking part in a top-notch technology team
Affecting the product roadmap by providing your feedback as a potential end user of the platform

We are looking for a Senior Backend Developer to join the hunt.
A bit about the position:
Major role in architecting and developing our Dev-Native Observability Platform.
Taking part in a top-notch technology team
Affect the product roadmap by providing your feedback as a potential end user of the platform

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.

Required Machine Learning Engineer - AI Coding Agents & LLM Infrastructure
Tel Aviv
Full-time
A bit about us:
We are redefining how software gets built. Trusted by over 1M+ developers, we build AI-first developer experiences powered by state-of-the-art coding agents and code reasoning models. With support for 30+ programming languages and 15+ IDEs, our platform is pushing the limits of LLM-based software engineering - enabling teams to design, write, review, and ship code faster than ever. Were committed to advancing code-native AI models, multi-agent systems, agent orchestration frameworks, memory, and autonomous dev tooling to empower developers at every step of the software lifecycle.
Were growing fast, and our team is passionate about pushing AI engineering to new heights - solving complex problems in LLM training, inference optimization, reasoning, and agent orchestration at scale.
About the Role:
As a Machine Learning Engineer, youll work on cutting-edge
code-focused LLMs and AI agent systems
that power our next-generation developer platform. Youll be at the center of research, model training, and productionization of intelligent systems that understand software deeply, collaborate with developers, and help automate engineering workflows end-to-end. Your work will immediately impact millions of engineers worldwide.
Responsibilities:
Push LLM Innovation: Research, design, and fine-tune domain-specific LLMs for code generation, refactoring, debugging, and multi-turn reasoning.
Agent-Oriented Development: Build multi-agent coding systems that integrate retrieval-augmented generation (RAG), code execution, testing, and tool use to create autonomous, context-aware coding workflows.
Production-Grade AI: Own the training-to-inference pipeline for large code models-optimize inference with quantization, distillation, and caching techniques.
Rapid Experimentation: Prototype and validate ideas quickly; leverage reinforcement learning, human feedback, and synthetic data generation to push accuracy and reasoning.
Cross-Functional Collaboration: Partner with product, engineering, and design teams to ship AI-powered features that help developers focus on high-impact work.
Scale the Platform: Contribute to distributed training, scalable serving systems, and GPU/TPU-efficient architectures for ultra-low-latency developer tools.

Required AI Engineering Team Lead - Applied AI Engineering Group
Tel Aviv Full-time
The Dream Job
It starts with you - a technical leader driven to build both the agentic AI platform and the engineering team behind it. You care about backend quality, platform reliability, and growing engineers through real ownership. We are AI-first across the board - every team builds and operates agents. You'll set the technical direction for the platform that makes this possible: agent orchestration frameworks, LLM gateways, evaluation infrastructure, tool-calling systems, and retrieval pipelines. Without this platform, agents don't ship - you own the layer that turns AI research into Sovereign AI products, deployed across cloud and on-prem environments. You stay close enough to the codebase to debug production incidents, unblock your engineers, and make sound architecture calls.
If you want to make a meaningful impact, join our mission and lead the team that builds the agentic AI platform driving Sovereign AI products - this role is for you.
The Dream-Maker Responsibilities
Architect and evolve the AI platform - agent orchestration, LLM gateways, context engineering pipelines, evaluation infrastructure, tool-calling systems, and retrieval pipelines - through RFCs, prototypes, and design reviews.
Lead and grow a small team of AI Engineers building the agent framework, production backend services, and AI platform infrastructure - hire, mentor, pair on hard problems, and raise the bar through hands-on code and design reviews.
Contribute to critical systems, debug production incidents, and maintain enough codebase context to make sound technical calls.
Own reliability across AI and agent services - set and enforce SLAs, build observability for non-deterministic systems, and harden tool execution environments for cost and security.
Set the standard for AI engineering practices - agent testing strategies, evaluation frameworks with human-in-the-loop oversight, retrieval quality benchmarks, and CI/CD for AI systems.
Work closely with ML Platform, Data Platform, DevOps, Data Science, and Product teams across the Applied AI Engineering group - ensure the AI platform evolves to serve teams building agentic workflows across the organization.
Measure and improve developer experience - deploy friction, onboarding time, CI turnaround - as seriously as system performance.

We are seeking an Associate Generative AI Engineer to join our AI squad . This role blends hands-on engineering with exposure to system design and client-facing delivery, making it an excellent opportunity for engineers early in their career to work on real, production-grade GenAI systems.
You will design, build, and operate LLM-based and agentic systems as part of cross-functional teams, contributing to both greenfield initiatives and the evolution of existing GenAI platforms. You will develop strong foundations in system architecture, backend engineering, and cloud-based GenAI delivery while working closely with more experienced engineers.
Key Responsibilities:
GenAI Development & Implementation:
Develop end-to-end GenAI solutions from POC through production deployment
Implement backend microservices and GenAI components using Python
Contribute to the development of multi-agent systems, orchestration layers, and autonomous workflows
Integrate and optimize LLMs and GenAI APIs within larger system
Participate in evaluating and improving system performance, scalability, reliability, and cost efficiency
Client Engagement & Collaboration:
Participate in technical discussions with clients and contribute to solution design discussions
Support presentations, demos, and technical explanations for client stakeholders
Collaborate closely with project managers, full-stack developers, and automation teams to deliver end-to-end solutions
Cloud & Platform Work:
Deploy and operate GenAI systems on GCP, Azure, and/or AWS
Work with cloud-native AI services and managed platform
Contribute to monitoring, reliability, and operational stability of production environments

Were looking for driven and talented people like you to join our R&D team and our mission to change the future of cloud security. Ready to dive in and swim with our pod?
As a Senior Software Engineer - AI , youll design, build, and own production grade AI agents that operate at the core of cloud security platform. Youll work on distributed, cloud native services that embed agentic AI workflows into existing microservices architecture.
This role goes beyond building AI logic: youll be responsible for operating AI systems in production, ensuring they are observable, reliable, and continuously improving through systematic evaluation and data driven iteration.
On a typical day youll:
Design and implement cloud-native, distributed services that power AI-driven security features
Build and maintain agentic AI systems that reason over large-scale cloud security data and interact with multiple internal services
Own AI agents in production, including deployment, monitoring, troubleshooting, and performance optimization
Implement observability for AI systems, including metrics, logging, tracing, and alerting for agent behavior, quality, latency, and cost
Develop continuous evaluation pipelines for agentic solutions, including offline testing, regression detection, and production feedback loops
Design and optimize RAG pipelines that operate reliably over high-volume, high-variance security data
Apply strong software engineering practices: clear APIs, clean abstractions, robust error handling, and scalable data flows
Lead services end to end - from design and implementation to deployment and long-term operation
Collaborate closely with Data Platform, Product, and Security Research teams to ensure AI behavior is correct, explainable, and trustworthy

Required xEngineer (ai-native software engineer)
makes it possible for anyone to succeed online. since 2006, weve grown to around 5,500 employees in 22 countries, launched over 40 products, and serve over 243 million users and their visitors worldwide. here, we push you to innovate, evolve in non-traditional ways, and collaborate. we operate in small teams that work closely together at a high level.
although were already industry leaders in web creation and business, our work is nowhere near done. our goal is to become the main engine of the internet so we can create a more open web for all.
we're proud to be an equal opportunity employer. the company was built around the idea that everyone has the right to be successful, online. this same vision defines us as an employer: creating a work environment where everyone is welcome, and anyone has the right to succeed.
as an xEngineer, youll combine deep technical expertise with system -level thinking and product ownership, applying ai across the development lifecycle to build impactful systems, make thoughtful architectural decisions, and drive technical excellence from idea to production. in your day-to-day, you will:
design and own end-to-end systems and features that serve millions of users, taking responsibility across stack boundaries - from product flows and User Experience to apis, data models, and production systems
apply ai-powered tools and workflows across planning, design, implementation, testing, debugging, and refactoring
work with modern technologies and platforms (e.g., typescript, React, Node.js, JAVA, cloud, and ci/cd), continuously evolving your toolkit with ai-powered tools
lead architectural thinking by making thoughtful trade-offs around scalability, reliability, security, performance, and maintainability
collaborate closely with product managers, designers, and engineers to shape solutions while ensuring production quality, observability, and long-term system health
at the company, we believe our best work happens together. our work model is fully in person, with 5 days a week from our office. flexibility remains a core value at the company and special requests are handled thoughtfully at the team level.
we are engineering at the company - a team of ai-native xengineers with deep expertise in frontend, backend, or mobile, who use ai support to take ownership over projects end-to-end. we work in a fast-paced, dynamic development environment with autonomous teams that never compromise high quality standards. as ai increasingly generates production code, we dedicate more and more of our time developing the skills we believe are critical for today and tomorrow: system design, architecture, scalability, reliability, and security.

We are seeking We are seeking an experienced Senior Generative AI Engineer (LLMs & Agents) to join our AI squad at KPMG. This role blends deep hands-on engineering with architectural responsibility and client-facing advisory work.
You will design, build, and operate production-grade LLM and multi-agent systems, working on both greenfield initiatives and the evolution of existing GenAI platforms.
You will play a key role in shaping technical direction, best practices, and delivery standards across the GenAI practice.
Key Responsibilities:
GenAI Development & Implementation
Build end-to-end GenAI solutions from POC through production deployment
Design and implement backend microservices architectures for GenAI applications using Pytho
Design, implement, and maintain production-grade Python services with a focus on code quality, performance, and reliability
Architect and develop multi-agent systems, orchestration layers, and autonomous workflows
Integrate and optimize LLMs and GenAI APIs across complex systems
Evaluate and improve system performance, scalability, reliability, and cost efficiency
Client Engagement & Advisory
Lead technical discussions with clients and translate business needs into technical architectures
Present GenAI solutions, design decisions, and trade-offs to technical and non-technical stakeholders
Provide strategic technical guidance on GenAI adoption and system design
Cloud & Platform Ownership
Deploy and manage GenAI systems across GCP, Azure, and AWS
Leverage cloud-native AI services (Vertex AI, Azure OpenAI, SageMaker, etc.)
Own production environments, monitoring, and operational excellence
Continuous Learning & Practice Development
Evaluate emerging GenAI models, frameworks, and techniques
Define and refine best practices for GenAI system development and deployment
Contribute to internal accelerators, methodologies, and knowledge sharing

We're looking for a AI Research Scientist to join us in spreading the power of our company and push the boundaries of AI in cybersecurity. You'll be at the forefront of developing novel AI models - focusing transformers and LLMs - that advance how cybersecurity operates. Contribute to the wider AI and cyber communities through publications and conference presentations, establishing our company as a thought leader at the intersection of AI and cybersecurity.
WHAT YOULL DO
Develop and research novel AI models, focusing on transformers and deep learning. Lead mid- to long-term projects aimed at advancing AI-driven features in the cybersecurity domain for our company products.
Conduct experiments and evaluate the performance of AI models, algorithms, and techniques using real-world datasets and simulated environments.
Stay informed about cutting-edge AI methodologies, frameworks, and tools and apply them to improve security solutions accuracy, efficiency, and scalability.
Collaborate with engineering teams to design, build, and maintain production pipelines. Work closely with the Security Research and Product teams to define research goals.
Publish research findings and AI thought leadership content and present at leading conferences such as NeurIPS, ICML, or CVPR.