דרושים » תוכנה » MLOps Engineer

we are looking for a MLOps Team Lead.
As an MLOps Team Leader , you will own our AI infrastructure and backend engineering efforts, leading a team at the intersection of systems design and machine learning to build the engineering systems that turn Deep Learning and Computer Vision research into reliable, scalable production features.
What Youll Be Doing:
Lead MLOps Engineering: Own the roadmap and execution for a team of backend and AI infrastructure/MLOps engineers, setting technical direction, removing blockers, and holding the bar on delivery quality.
Build Production AI Systems: Design and implement production-grade, end-to-end AI solutions, including agentic workflows, that integrate Deep Learning models and Computer Vision algorithms into real features. Own the offline experimentation and validation layer, including POC environments that let the Algo team move from research to production confidently.
Architect Data Platforms: Drive the design and implementation of scalable data platforms and pipelines that power AI capabilities reliably and with an eye on cost and scale.
Cross-functional Collaboration: Work closely with the Algorithms and Data teams to tackle complex, real-world problems, translating research into shippable, maintainable systems.
Elevate Engineering Standards: Champion Software Engineering and System Design best practices across the group, introducing the right methodologies, tooling, and culture of craft.
Who You Are:

We're looking for a hands-on Senior backend Engineer to join the App Studio & Intelligence team within our company's GenAI Group. Our group builds the company's core Generative AI products - including a company-wide agents platform and customer-facing AI assistants - enabling teams across our company and our customers to design and deploy AI-driven solutions. In this role, you'll design and build scalable microservices and cloud-native infrastructure at the heart of our GenAI applications, working with advanced AI capabilities such as knowledge bases, embeddings, LLM orchestration, agent frameworks, and evaluation systems. You'll be a senior technical voice on the team - owning complex features end-to-end, raising the engineering bar, and shaping how AI capabilities are delivered in production.
?What You'll Do Design, build, and own backend services and infrastructure for our GenAI applications - scalable, resilient, production-grade systems running on AWS. Build the APIs, data pipelines, and backend integrations that power advanced AI capabilities, including knowledge bases, embeddings, LLM orchestration, agent workflows, and evaluation frameworks, with attention to performance, scalability, and security. Lead complex design efforts - driving architecture discussions, design reviews, and technical decisions for significant areas of the system. Collaborate closely with product, data science, and other engineering teams to translate business needs into robust backend solutions that enable AI-agent use cases. Raise the engineering bar through code reviews, mentoring less experienced engineers, and championing best practices in quality, observability, and operational excellence. Take ownership of production health, including monitoring, alerting, troubleshooting, and continuously improving system reliability.
Position Intro:
we are the premier provider of mission-critical, cloud-based intelligent decisioning across pricing, rating, underwriting, and product personalization. These fully-integrated solutions provide ultra-fast ROI and are designed to transform how global insurers and banks are run by unlocking value across all facets of the business. our company has been innovating for insurers and banks since 2001 with customers in over 35 countries across six continents and offices in the Americas, Europe, Asia Pacific, and Israel.

We're looking for a hands-on Senior backend Engineer to join the App Studio & Intelligence team within our company's GenAI Group. Our group builds the company's core Generative AI products - including a company-wide agents platform and customer-facing AI assistants - enabling teams across our company and our customers to design and deploy AI-driven solutions. In this role, you'll design and build scalable microservices and cloud-native infrastructure at the heart of our GenAI applications, working with advanced AI capabilities such as knowledge bases, embeddings, LLM orchestration, agent frameworks, and evaluation systems. You'll be a senior technical voice on the team - owning complex features end-to-end, raising the engineering bar, and shaping how AI capabilities are delivered in production.
?What You'll Do Design, build, and own backend services and infrastructure for our GenAI applications - scalable, resilient, production-grade systems running on AWS. Build the APIs, data pipelines, and backend integrations that power advanced AI capabilities, including knowledge bases, embeddings, LLM orchestration, agent workflows, and evaluation frameworks, with attention to performance, scalability, and security. Lead complex design efforts - driving architecture discussions, design reviews, and technical decisions for significant areas of the system. Collaborate closely with product, data science, and other engineering teams to translate business needs into robust backend solutions that enable AI-agent use cases. Raise the engineering bar through code reviews, mentoring less experienced engineers, and championing best practices in quality, observability, and operational excellence. Take ownership of production health, including monitoring, alerting, troubleshooting, and continuously improving system reliability.
Position Intro:
we are the premier provider of mission-critical, cloud-based intelligent decisioning across pricing, rating, underwriting, and product personalization. These fully-integrated solutions provide ultra-fast ROI and are designed to transform how global insurers and banks are run by unlocking value across all facets of the business. our company has been innovating for insurers and banks since 2001 with customers in over 35 countries across six continents and offices in the Americas, Europe, Asia Pacific, and Israel.

We are looking for a Sr. Staff Backend Engineer to join our team. This is a Hybrid (Ramat Gan) role, reporting to the Manager, Software Engineering in the Exposure Management & Security Operations department. we power the data foundation behind our Security Operations platform, ingesting, unifying, and contextualizing data from hundreds of security and business tools to power security modules that deliver actionable insights and measurable risk reduction to our customers. In this role, you'll own complex backend systems that sit at the heart of our data platform, designing and building services that ingest, process, and move massive amounts of data reliably and at scale for enterprise customers worldwide.
What youll do (Role Expectations)
Developing and maintaining high-quality, scalable backend services within a microservices architecture using Java/Spring Boot and Python
Designing and building data pipelines and ETL workflows that ingest, transform, and unify data from diverse sources at enterprise scale
Leading solutions from scratch to production; creating technical designs, driving architectural decisions and taking full ownership of complex projects - from design and implementation through deployment, monitoring performance, reliability and cost, and proactively driving improvements
Collaborating closely with cross-functional partners - product managers, frontend/backend engineers and UX designers
Serving as a technical anchor for the team - leading design discussions, setting engineering standards, and driving best practices across the codebase

Join our dynamic engineering team as a Backend Engineer, where you will contribute significantly to the development of robust backend solutions and product innovations. If you have a passion for software engineering, enjoy tackling challenges, and view software development as an art form, you might be the perfect fit for our team.
What you'll be doing -
Backend System Development - Use cutting-edge technologies to develop and refine backend systems integral to our products functionality and user experience.
Product-Driven Development - Play an active role in a product-focused squad, contributing to projects that align with the companys vision and the products roadmap.
Engineering Project Participation - Take part in significant engineering projects, applying best practices in software craftsmanship to deliver high-quality solutions.
Technology Stack - Work with a broad range of technologies including Kubernetes, Kafka, RabbitMQ, Materialize, Postgres, Rust, Clojure, and Elixir to design and scale backend systems.
Collaboration and Integration - Collaborate with various teams within the company, contributing to essential microservices and APIs that underpin the product.
Backend Guild Engagement - Actively contribute to a backend guild that drives best practices, guidelines, and standards across the R&D team.

We are a fast-growing cybersecurity startup redefining how organizations protect themselves. Our Data Team is at the heart of that mission - powering everything from security insights to customer-facing intelligence. We are looking for a Software Engineer to help design and develop the core services and applications that drive our data capabilities. In this role, you will build high-quality, scalable software; contribute to the architecture of our next-generation data systems; and collaborate with research and product teams to turn cutting-edge ideas into production-ready features.
Responsibilities:
Design, develop, and maintain scalable backend services and components that power our data workflows, analytics, and product features.
Build high-quality internal tools and applications that improve engineering workflows and enable data-driven development across the organization.
Contribute to the architecture and evolution of our new data platform with a strong focus on clean design, testability, maintainability, and performance.
Collaborate closely with security researchers, analysts, and product managers to translate innovative cybersecurity concepts into reliable, production-ready software.
Apply engineering best practices across the stack - including code quality, testing, observability, versioning, and documentation - to ensure system robustness as we scale.

Were looking for an AI Software Engineer to own the RL Gym platform end-to-end: from architecting multi-site web environments that simulate real-world attack surfaces, to optimizing our in-house orchestration harness (AgenticVerse) for high-performance delivery into customer training pipelines.
This is a builder role. Youll lead a small team (including a dedicated web environments engineer), operating with high autonomy, moving fast from concept to working prototype to production system. Youll interact directly with customer engineering teams to understand their infrastructure constraints and deliver environments that meet their scale and reliability requirements.
Why this role:
This is one of the few roles in the industry where your code directly influences how the next generation of AI models are trained. Youll be at the center of advancing AI safety, building systems that the worlds top labs depend on to make their models more robust. The work is technically deep, the problem space is genuinely novel, and the field is moving faster than any team can keep up with alone. Theres no playbook. Youll write it.
What youll do:
Platform & performance:
Own and evolve AgenticVerse, our in-house orchestration harness that provisions and manages RL environments at scale. Focus on performance: low-latency provisioning, high concurrency, minimal overhead per environment instance
Design and build isolated, reproducible web environments using Firecracker microVMs or Docker containers
Architect multi-site scenarios (3-4 interconnected web applications per task) with rich interactions: drag-and-drop, file uploads, authentication flows, LLM-in-the-loop components
Implement deterministic verifiers that evaluate agent behavior with zero ambiguity
Customer delivery:
Work directly with engineering teams at leading AI labs to integrate RL Gym environments into their training and evaluation pipelines
Translate customer specs into working environments, iterating rapidly on feedback
Own the technical relationship: SLAs, API contracts, integration architecture
Adapt environment delivery formats to cus tomer infrastructure (real-time API calls vs. offline batch, managed vs. raw artifacts)
Build customer-facing UIs when needed (dashboards, environment configuration portals, monitoring interfaces)
Rapid prototyping:
Take ambiguous problem descriptions and produce working prototypes within days, not weeks
Validate new environment types, interaction patterns, and verifier approaches quickly
Build internal tooling that accelerates scenario authoring and testing.

We are looking for a Staff Architect, Data & AI Infra to shape, build, and scale the infrastructure that powers our data, AI, and research platforms. This is a senior player-coach role with broad architectural ownership across data infrastructure, ML infrastructure, developer experience, reproducibility, and production reliability. You will work across the wider engineering group as a hands-on technical architect, while also managing a small team of individual contributors focused on ML infrastructure.

This role is ideal for someone who can move between long-term platform architecture and practical execution: defining standards, building core systems, mentoring engineers, improving reliability, and partnering with Data Engineering, AI/Research, Product Engineering, Security, Bioinformatics, and Leadership to make our data and AI platforms scalable, reproducible, secure, compliant, and easier to use.

Location: Ramat Gan, Israel (hybrid model)

What will you do?

Architectural Leadership: Own and evolve the technical roadmap for our data and AI platforms, ensuring scalable and reliable architecture that supports current needs and prepares for a multi-cloud future.
MLOps & Platform Development: Design and build end-to-end MLOps systems-covering experimentation, training, reproducibility, and deployment-while managing specialized infrastructure like BigQuery, orchestration tools (Dagster/Airflow), and R/Python workloads.
Infrastructure Strategy: Define and lead strategy for GPU resources (scheduling, utilization, batch compute) and establish engineering best practices, data architecture standards, and platform guardrails.
Developer Experience: Enhance developer productivity by building self-service platforms, automation, internal tooling, and reusable templates that simplify workflows and reduce operational friction.
Team Leadership: Act as a player-coach to mentor engineers and manage a small team of ICs, fostering a culture of sound decision-making and technical excellence across the broader group.
Security & Reliability: Partner with Security to enforce compliance (SOC2, HIPAA, GDPR) and access controls, while mitigating operational risk through improved observability, incident readiness, and robust support processes.

You will build globally distributed, fault-tolerant, and highly scalable identity threat detection systems that analyze billions of authentication events per day to detect sophisticated Active Directory attacks, credential theft, and lateral movement across hybrid and multi-cloud (Azure/Entra, Okta, AWS) environments. You'll design, lead, and implement the next generation of our Identity Protection detection platform - evolving it from on-prem appliances toward cloud-native, event-streaming microservices.

What You'll Do

Design and implement real-time detection rules and pipelines over high-throughput Kafka event streams, maintaining sub-second detection latency at massive scale.

Build the pluggable detection engine and rule framework that turns raw authentication telemetry into indicators, and indicators into enriched, customer-facing alerts.

Develop machine-learning and behavioral-anomaly models, and integrate LLM-based detection reasoning (e.g. AI-driven severity scoring) into production detection flows.

Translate security research into production-grade detections, partnering with security researchers who guide you on the threat landscape.

Own detections in production: observability, latency SLOs, safe per-customer rollouts (feature-flag-gated), and incident response for the detection pipeline.

Work across a polyglot stack - Python for detection logic and ML, Go for the high-performance streaming services, Java for backend alert management.


This position is based in our Ramat-Gan, Israel office and requires strong leadership presence and ability to work closely with cross-functional teams.

We are looking for an ML Infra Engineer to play a central role in evaluating, and deploying 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.

This is NOT a core algorithmic research role - but rather, a role focused on building out the ML engineering infrastructure for the evaluation and deployment of models.

Location: Ramat Gan, Israel (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.