לוח משרות דרושים AI Engineer ברעננה, לחיפוש זה נמצאו 11 משרות

We're looking for a Senior AI Infrastructure Engineer to join a group that specializes in Security and Networking, and specifically ML/AI, MLOps, and agentic AI development. As a Senior AI Infrastructure Engineer, youll build and maintain the infrastructure, tools and processes necessary to support the AI lifecycle in a production environment. You will collaborate closely with data scientists, software engineers, and security architects to ensure smooth development, deployment, evaluation, and optimization of AI pipelines, models, and agents. This role requires a balance of high-level engineering rigor and a collaborative spirit; youll be a technical anchor and a supportive peer for teams across the organization.



What youll be doing:

Architecting, developing and optimizing scalable infrastructure for deploying security and networking AI models and agents in production.

Managing ML/agentic workflows to ensure performance, high availability, resource efficiency, and cost-effectiveness.

Designing and implementing pipelines and frameworks for AI training, inference, and experimentation.

Partnering with data scientists and security architects to operationalize AI agents, including packaging and integration with existing systems. This includes contributing to and reviewing code, design documents, and test plans.

Partnering with DevOps teams to integrate pipelines and workflows into CI/CD processes, ensuring reliable deployments and rollbacks.

Building proactive monitoring systems to identify issues in quality and infrastructure before they impact production.

Implementing access controls, authentication mechanisms, and encryption standards to keep our AI models and data secure.

Documenting guidelines and leading knowledge-sharing sessions to elevate the teams collective development expertise.

In this role, you will be shaping the future of our companys AI software stack for Neural Processing Units (NPUs). You will lead software architecture, define performance-critical components, and enable efficient execution of advanced neural networks under tight power, memory, and latency constraints.
You will work closely with hardware and system architects, software and hardware engineers, influencing both software and hardware decisions. You will design and implement major parts of our company NPU embedded solutions, actively promoting our company AI capabilities to the customers.
We are seeking a high-impact motivated Software Architect to join our team and help shape the future of our advanced neural network AI Software Toolchain over our companys AI computing processors.
Responsibilities
Lead Software Architecture specification and supervise design for the most advanced Software Toolchain handling state-of-the-art Neural Processing Unit operation.
Design and promote holistic software solutions incorporating various aspects of NPU handling: from network retraining, graph compiling and network inference execution on the embedded systems.
Collaborate proactively with Product, Architecture, VLSI and Software teams to promote software leadership over various markets and compute SoCs.
Explore and translate state-of-the-art neural network and AI applications requirements into software architecture flows, encompassing hardware, software, tools and other components.
Evaluate architecture proposals, internal and external IP features and provide influential and inspirational leadership across hardware and software to align all parties to a common vision of architecture & technology development.
Represent our company with high technical credibility in customer meetings, appropriately incorporating feedback.
Boost velocity of development teams by providing technical guidance and by constantly looking ahead to anticipate and resolve future challenges.
Conduct experiments, invent and drive development of supporting tools such as simulators, models, profilers, and other methods as required.
Engage with engineering leadership and product planning stakeholders to develop technology roadmap.

In this role, you will be a key contributor to the design and implementation of our companys AI Graph Compiler software stack for Neural Processing Units (NPUs). You will take part in defining software architecture, implementing performance-critical components, and enabling efficient execution of advanced neural networks under tight power, memory, and latency constraints.
You will work closely with hardware and system architects, software and hardware engineers, influencing both software and hardware decisions. You will design and implement major parts of our company NPU embedded solutions, actively promoting our company AI capabilities to the customers.
What will you do:
Own and design key components of the AI Graph Compiler software stack for NPU-based systems.
Optimize inference performance (latency, throughput, memory footprint, power) for edge deployments.
Collaborate on HW-SW co-design, influencing NPU architecture.
Support IP evaluations and silicon bring-up, root-cause complex HW/SW issues, and influence development methodologies.
Mentor junior engineers and contribute to technical best practices.

In this role, you will be a key contributor to the design and implementation of our companys AI Graph Compiler software stack for Neural Processing Units (NPUs). You will take part in defining software architecture, implementing performance-critical components, and enabling efficient execution of advanced neural networks under tight power, memory, and latency constraints.
You will work closely with hardware and system architects, software and hardware engineers, influencing both software and hardware decisions. You will design and implement major parts of our company NPU embedded solutions, actively promoting our company AI capabilities to the customers.
What will you do:
Own and design key components of the AI Graph Compiler software stack for NPU-based systems.
Optimize inference performance (latency, throughput, memory footprint, power) for edge deployments.
Collaborate on HW-SW co-design, influencing NPU architecture.
Support IP evaluations and silicon bring-up, root-cause complex HW/SW issues, and influence development methodologies.
Mentor junior engineers and contribute to technical best practices.

In this role, you will learn and deeply understand the AI fundamentals and create tools and software that transform AI network into embedded implementation on our companys processors. In addition, you will perform in-depth analysis and optimization to the generated software and tools, to ensure the best performance and accuracy on various our company s processors architectures.
Responsibilities:
Taking part of innovative, agile, top-notch SW team, designing Neural Network Processor (NPU) graph compiler and software in close collaboration with the architecture, algorithms, system and hardware teams.
In addition You will take part in designing and developing software code for graph compiler for neural network processing and embedded system resources management.

In this role, you will be a key contributor to the design and implementation of our companys Edge AI software stack for Neural Processing Units (NPUs). You will take part in defining software architecture, implementing performance-critical components, and enabling efficient execution of advanced neural networks under tight power, memory, and latency constraints.
You will work closely with hardware and system architects, software and hardware engineers, influencing both software and hardware decisions. You will design and implement major parts of our company NPU embedded solutions, actively promoting our company AI capabilities to the customers.
What will you do:
Own and design key components of the Edge AI software stack for NPU-based systems.
Optimize inference performance (latency, throughput, memory footprint, power) for edge deployments.
Collaborate on HW-SW co-design, influencing NPU architecture.
Support IP evaluations and silicon bring-up, root-cause complex HW/SW issues, and influence development methodologies.
Mentor junior engineers and contribute to technical best practices.

we are looking for a AI Automation Architect.
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.

we are looking for a AI Quality Architect.
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.

We are looking for a Senior Backend Engineer specialized in Generative AI to design agent workflows, optimize interactions with models (OpenAI, AWS Bedrock), and ensure the reliability of non-deterministic systems in production.

Tech Stack: Python (Asyncio), FastAPI, LangChain, LangGraph, Pydantic, Elasticsearch, AWS Bedrock / OpenAI API, LangSmith.

What You'll Do
Agent Architecture: Design and implement complex agent orchestration logic using LangGraph. You will define state management, conditional routing, and error handling within the agent graph.
Tool Engineering: Build and optimize the tool layer (function calling) that allows LLMs to interact with internal financial APIs and databases accurately.
Performance Optimization:
-Reduce end-to-end latency through asynchronous processing and streaming (SSE).
-Implement semantic caching strategies to minimize API costs and response time.
-Optimize token usage without sacrificing answer quality.
Observability & Evaluation: Implement automated evaluation pipelines using LangSmith. You will be responsible for setting up regression testing for prompts and agents to measure quality (correctness, faithfulness) before deployment.
Advanced RAG: Refine retrieval strategies. Work on hybrid search implementation (Keyword + Vector), re-ranking, and query expansion to feed the most relevant context to the model.