We are looking for a talented and motivated Software Engineer with hands-on experience building and operating multi-agent AI systems in production to join our Automation Platform (DAP) team.
The team develops automation tools, orchestration capabilities, and intelligent platforms that simplify the deployment, management, troubleshooting, and optimization of large-scale network and AI infrastructure environments.
You will work on the design and development of AI-powered systems that bridge networking, automation, observability, and distributed infrastructure - running on Kubernetes at scale. Our stack includes LangGraph, Langfuse, RAG pipelines, MCP, and agent-to-agent (A2A) communication patterns.
This role combines strong software engineering with practical AI application development, with a sharp focus on production hardening, tracing, evaluation, and safety of agentic systems - not model training or research prototypes.

We are looking for a Principal MLOps Engineer with a deep focus on ML Platforms and Infrastructure to join our Data & AI group at Cortex Research. Our team is responsible for designing, building, and scaling the foundational MLOps and LLMOps platforms that power both our Data Scientists and Security Researchers. You will architect the high-performance core infrastructure that enables these roles to build, train, and deploy advanced AI systems-ranging from optimized Small Language Models (SLMs) to complex agentic workflows and RAG systems. If you are passionate about building scalable compute platforms and automating the full ML lifecycle to solve complex data and security challenges, we want to hear from you.
Key Responsibilities
Scale Distributed Training: Design and optimize infrastructure for training and fine-tuning LLMs and SLMs, leveraging distributed GPU workloads, efficient clustering, and compute optimization.
Automate the ML Lifecycle: Architect robust, automated pipelines for continuous training (CT) and deployment (CD) of models, ensuring a seamless flow from raw data collection to production environments.
Build Model Infrastructure: Own the serving architecture for LLMs/SLMs, balancing latency, throughput, and GPU utilization under production traffic.
Implement Advanced Monitoring: Establish comprehensive observability systems to monitor live model performance, data drift, and computational metrics, feeding insights back into the automated training loops for continuous improvement.
Collaborative Architecture: Partner closely with data scientists and security researchers to productize complex model architectures and streamline their workflows, while collaborating with our DevOps team to integrate with core cloud infrastructure.

We are looking for an experienced MCP (Model Context Protocol) Software Engineer to design, develop, and optimize Model Context Protocol implementations. You'll work on building robust MCP servers, client integrations, and tools that allow AI systems to securely access and interact with various data sources and services.

What You'll Do:

MCP Development & Implementation
Design and develop MCP servers for various data sources (databases, APIs, file systems, cloud services).
Build and maintain MCP client integrations for AI applications and platforms.
Implement secure authentication and authorization mechanisms for MCP connections.
Develop custom MCP tools and resources to extend AI capabilities.

Architecture & Design
Architect scalable MCP solutions that handle high-volume data access patterns.
Design efficient protocol implementations that minimize latency and maximize throughput.
Create reusable MCP components and libraries for internal and external use.
Establish best practices for MCP server development and deployment.

Integration & Collaboration
Integrate to our identity platform.
Work with DevOps teams to deploy and monitor MCP services in production environments.
Partner with security teams to ensure MCP implementations meet compliance and security requirements.
Integrate MCP solutions with existing infrastructure and third-party services.

Quality & Performance
Write comprehensive tests for MCP implementations including unit, integration, and end-to-end tests.
Monitor and optimize MCP server performance and resource utilization.
Debug complex issues in distributed MCP environments.
Maintain detailed documentation for MCP servers and integration patterns.

Contribute to Open Source
Engage with the open-source community, contributing to the development and improvement of MCP.

Where You'll Be: Tel Aviv | Full-time, hybrid.

Were looking for an Engineering Team Lead, who will be responsible for the foundational infrastructure framework used by all our company engineering teams to build, deploy, and operate AI agents safely in production. We are building the "operating system" for AI at our company, covering agent sessions, memory management, tool orchestration, durable execution, and multi-tenant isolation. You will lead a high-impact team of 5 engineers to create the runtime and platform that defines the future of autonomous enterprise intelligence.
Youll Own:
Agentic Framework Architecture: Designing and building our companys internal agentic framework, leveraging and integrating industry-standard tools such as LangChain, LangSmith, ADK, and similar ecosystems.
Evaluation and Quality Systems: Building evaluation frameworks and workflows for AI agents, including offline and online evaluations, quality metrics, regression detection, and experimentation infrastructure.
Team Leadership & Mentorship: Leading a squad of 3-4 senior engineers, fostering a culture of technical excellence, and managing end-to-end delivery in a fast-paced environment. You will spend approximately 50% of your time hands-on, architecting core systems and reviewing code, and 50% leading the team, mentoring engineers, and aligning with cross-functional stakeholders.
Observability, Monitoring, and Guardrails: Providing the organization with robust observability capabilities for AI agents, including tracing, logging, monitoring, cost tracking, and safety guardrails to ensure reliable and responsible usage.
Developer Enablement Platforms: Creating APIs, SDKs, and abstractions that enable product teams to easily build, test, and operate agents while adhering to platform standards.
Cross-Language Integrations: Designing integrations and tooling across Python and Java to enable seamless adoption of the AI framework within our companys broader backend ecosystem.
Youll Solve:
Agent Lifecycle and Orchestration Complexity: Managing agent execution, tool usage, memory, workflows, and failure modes in production-grade systems.
AI System Reliability at Scale: Ensuring agents remain observable, debuggable, and safe as usage scales across teams and products.
Evaluation and Drift Challenges: Detecting quality regressions, model behavior changes, and unintended agent behaviors through robust evaluation and monitoring systems.
Platform Adoption Friction: Balancing flexibility with guardrails so teams can innovate quickly without compromising reliability, security, or cost controls.
Youll Impact:
Company-Wide AI Enablement: Empowering every engineering team at our company to build agent-based solutions faster, with higher quality and confidence.
Foundational AI Infrastructure: Establishing the core frameworks, evaluations, and observability standards that all AI agents at our company will rely on.
AI Safety and Quality Bar: Raising the bar for how AI systems are evaluated, monitored, and governed across the company.

We're looking for a Senior Fullstack Software Engineer to build intelligent backend infrastructure and exceptional user experiences that power our platform. You'll work directly with LLMs, design scalable systems, and ship production AI features that transform how legal teams operate worldwide. If you love owning features end-to-end and moving fast, we want to hear from you.
What You'll Do
Own features end-to-end, from design through deployment across the full stack
Build scalable backend services and APIs using Python (FastAPI, Pydantic, Postgres, MongoDB)
Create responsive, performant frontend experiences with React.js
Develop integrations with enterprise platforms like Salesforce, Google Drive, SharePoint, and DocuSign
Work hands-on with LLMs and build the connecting logic between AI capabilities and business systems
Partner with ML engineers, AI researchers, and legal domain experts to deliver impactful solutions
Contribute to architectural decisions and help establish engineering best practices as we scale.

we are looking for a Senior Full Stack Engineer to join our Engineering team. In this role, you will lead the design and development of end-to-end features across our gifting platform, with a strong focus on backend architecture. Youll play a key role in shaping scalable systems, driving technical decisions, and delivering high-impact solutions in a fast-growing, cloud-based SaaS environment.
Working with modern technologies such as React, Node.js, TypeScript, Serverless architecture, and Kafka, you will collaborate closely with product, design, and data teams to build reliable, performant, and user-centric experiences. You will also help evolve our engineering standards, development processes, and use of emerging tools-including AI-to improve team productivity and product quality.

We're now expanding our Technology team with engineers who can build the next generation of AI-driven products at the core of our platform.

Why Join Us? This is a chance to build at the intersection of fintech and applied AI, not as a side experiment, but as the direction of the product. You'll design and ship production-grade backend and FullStack systems that sit at the heart of a real, global investment platform, while being hands-on building AI agents that teams across the company actually depend on: architecting scalable microservices in Node.js, putting LLMs and agentic tooling to work, and owning features end to end from design through deployment.
Because real users and real capital flow through our platform, your work ships and matters from day one. You'll get real ownership over the features you build, hands-on exposure to how we adopt agentic AI, and a front-row seat to the startup and innovation ecosystem we live inside every day. If you want to master cutting-edge AI tooling while solving hard distributed systems problems alongside a small, sharp team - this is the place to do it.
This role requires 3 days a week in-office with the technology team based out of our Tel Aviv office. Fluent English and Hebrew required: English for direct communication with manager who is non Hebrew-speaking, and Hebrew for in person team discussions.
A strong sense of system design is a big plus for this particular team in the Technology department.
Responsibilities:

* Design, build, and ship production backend and FullStack features for OurCrowd's investment platform using Node.js and microservices
* Build and deploy AI agents and LLM-powered tools used in production across the company, from prototype to rollout
* Architect and maintain scalable microservices, integrating SQL and NoSQL data stores as needed
* Design agentic workflows and orchestration pipelines, using low-code tools (e.g. n8n, Make) alongside custom services where appropriate
* Own features end to end: design, estimation, implementation, testing, deployment, and monitoring
* Partner with product, data, and engineering teams to turn business and investor needs into technical solutions
* Evaluate emerging LLM frameworks and AI tooling to keep our stack current and production-ready
* Write clean, well-tested code and participate in code reviews to uphold team quality standards
* Troubleshoot issues across the stack, balancing delivery speed with the reliability a capital platform demands
* Help shape architecture, tooling, and best practices as the team scales its use of agentic AI
Interview Process:

* Recruiter phone call
* Take home assignment
* Technical in-person interview with 2 Team Leads
*  system Design focused virtual interview with team lead (English only)
* In-person interview with CTO
* In-person interview with HR
* References

Were looking for a Senior MLOps Engineer to be a core driver in how our product empowers security teams. You will be expected to deeply understand customer needs and translate them directly into product features that deliver real value. You'll own key parts of our frontend stack, drive key architectural decisions, and turn complex security data into clear, actionable business insights.

As we scale our AIDR product and expand deeper into model-driven security intelligence, we are looking for a Senior MLOps Engineer to own the infrastructure, tooling, and operational foundations that power our NLP and LLM training, evaluation, and deployment workflows.

You will architect and operate the systems that enable us to train, fine-tune, deploy, and monitor models at scale making ML reliable, fast, cost-efficient, and production-ready.

This is a high-visibility, high-impact role where you will partner closely with DevOps, Backend, Data, and Product to establish world-class ML infrastructure from the ground up.

What Youll Do

Build & Scale ML Pipelines
Design, build, and maintain pipelines for training, fine-tuning, evaluating, and deploying NLP and LLM models across GPU and CPU environments.
Establish LLM-Focused CI/CD
Implement automated CI/CD workflows for ML models, including benchmarking, testing, performance gating, and production deployment.
Optimize Runtime & Inference
Select and optimize serving frameworks for low-latency, high-throughput inference, ensuring reliability and scalability.
Own ML Infrastructure
Manage training environments, experiment tracking, model registries, artifact versioning, and distributed training systems.
Operational Excellence
Monitor and optimize production models for performance, cost efficiency, availability, and observability.

We are looking for people who are passionate about leading technology to the extreme. If you are striving to develop an innovative product, make an impact, and work with the industrys brightest talent as part of one of the fastest-growing and successful startups around


What will you actually be doing?
End to end development of features and systems that are used daily by key stakeholders, both internally and externally.

A technical focal point for software development best practices and design of main components, services and systems.

Integrate AI into the company applications.

Improve development workflow with AI capabilities.

Be part of an organic team developing in a fast paced environment based on CI/CD.

Investigate, implement and share new technologies and ideas.