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We're looking for a System Architect to own the technical foundation end to end.

What you'll do

Own the architecture - a scalable, cloud-native system from whiteboard to production on a greenfield Azure environment you'll stand up yourself (Kubernetes-native, Everything-as-Code, GitOps, CI/CD from day one).
Work directly with our Chief AI Officer to translate complex decisioning methodologies into clean,self-service products a customer can run without a data-science team.
Lead the coding. Set the standards, make the hard calls, and write the core code yourself. You'll guide the engineers shoulder-to-shoulder - leadership by example, not by org chart, without managing them directly.
Stand up a fully agentic SDLC - building the product withAI coding agents end to end, so a small team ships like a large one.
Design the integration layer so the product plugs into each host's native agent and data surfaces - customers adopt it without leaving the tools they already use.

We are seeking an MLOps Engineer to help us grow our technical team's capabilities. The ideal candidate has relevant experience in data engineering, preferably within the AI field. Aviation industry experience would be a great addition.

You will be responsible for building and maintaining models and data pipelines that power our data science workflows. You'll play a crucial role in ensuring the accuracy, consistency, and efficiency of the data we use for model training and inference. This involves working with both structured and unstructured data from various sources, leveraging your expertise in data engineering and machine learning to create a robust and scalable system.

We're looking for a Data Scientist to help us grow our advanced analytics team's capabilities. The ideal candidate is a data scientist with a relevant experience who is self-driven, motivated, independent and sharp



Design and operationalize causal-uplift measurement frameworks to quantify the incremental impact of pricing models
Lead data discovery and translate complex multidimensional data into actionable insights related to the pricing algorithms and their outcomes
Conduct research and experimentation to elaborate novel approaches to uplift measuring and a/b testing
Collaborate with revenue managers, data analysts, data engineers in order to encode airline business logic into modeling and technical pipelines

We are looking for Customer Support Engineerin our site in Netanya with a strong data background, who can provide ongoing support to our client while monitoring our production pipeline ensuring we deliver within the agreed SLA . You will be an essential part of the frontline support team responsible for providing prompt and efficient support and service to end-users experiencing technical issues with software. The primary responsibility is to ensure the timely resolution of data-related problems and to facilitate effective communication between end-users and higher-level support teams. The ideal candidate has relevant experience in data engineering. This role involves coordinating with various teams, managing schedules, and ensuring that production processes run smoothly and efficiently.

Work schedule:

The employee will be required to work 5 days a week, spread across the entire week, including Saturday and Sunday, with off-days during weekdays if shifts fall on weekends. The job is shift-based, with shifts occurring both during the day/evening/night and on weekends.

What You'll Do:

You will be responsible for maintaining and monitoring data pipelines that power our data science workflows while developing and maintaining production schedules.
Identify potential production issues and work proactively to resolve them.
Communicate any production delays or issues to relevant stakeholders and customers.
Maintain accurate records of production activities, including schedules, bugs and project status.
Coordinate with various departments (e.g., Data, DE, PO) to ensure timely completion of tasks.
Serve as the frontline support and provide prompt and efficient support to end-users experiencing technical issues with the platform.
Troubleshoot problems and offer effective solutions to maintain optimal user experiences.
Collaborate with other teams, including R&D and product management, to resolve customer issues.

We are looking for an MLOps Team Leadto drive the development of an internal machine learning platform for a group of ML teams. This is a hands-on leadership role: you will guide a small team of MLOps engineers that builds the automation and infrastructure powering our research (R&D) workflows and runs our pipelines to production. You will take ownership of end-to-end initiatives and drive the team toward critical infrastructure and model-lifecycle milestones, while staying close enough to the code to set technical direction and raise the bar by example.

You will be responsible for building and maintaining the models and data pipelines behind our data science workflows, ensuring the accuracy, consistency, and efficiency of the data used for training and inference, working across structured and unstructured data from many sources on a large-scale, distributed platform.

Responsibilities:

Lead, mentor, and grow a team of MLOps engineers, owning delivery and technical quality.
Take end-to-end ownership of infrastructure and pipeline initiatives across the LMM group, from design through production.
Stay hands-on: contribute to design and code, review work, and set engineering standards.
Drive the team through critical milestones in ML model-lifecycle and infrastructure ownership.
Partner with R&D and other stakeholders to translate research needs into robust, scalable systems.
Help evolve the platform, including our ongoing migration from Dask to Ray.

We are seeking a talented and self-driven experienced Data Scientist to help advance our machine learning capabilities.This is a key role for someone passionate about leveraging machine learning to solve complex, real-world problems and deliver measurable business impact.

Responsibilities:

Develop and implement state-of-the-art econometric and machine learning models for demand forecasting.
Conduct research and experimentation to evaluate novel approaches for improving accuracy, robustness, and scalability.
Collaborate with cross-functional teams (including product, data engineering, MLOPS and Platform) to deploy ML systems in production.
Clearly communicate complex technical findings to non-technical stakeholders, including product leaders and executives.

Were looking for a Data Analyst with expertise in Tableau to join our growing data analytics team. This role is perfect for someone who thrives on turning data into actionable insights and enjoys working at the intersection of business and technology.

we are transforming our internal infrastructure into a high-velocity, self-service product. This is a senior opportunity to architect the future of our platform, setting the technical standard for how we deliver services while ensuring reliability and developer independence across diverse global markets.

Responsabilities:

Self-service enablement: Build secure, automated pathways that accelerate development while keeping organizational standards intact.
Architectural modularization: Design Kubernetes-native abstractions - controllers and operators - and decouple infrastructure into independent service lifecycles that support rapid iteration.
Everything as code: Treat infrastructure, policy, and configuration with the same SDLC as application software - versioned, reviewed, tested, and CI-gated.
Proactive reliability: Own production through robust observability, clear SLOs, and high operational hygiene; define alerting that fires correctly and lead blameless postmortems that fix the system.

We are seeking an experienced Data Science Team Lead to lead our data science and data engineering efforts and oversee a team of skilled data engineers. This role combines hands-on technical leadership and team management, with responsibilities that include building and scaling data infrastructure to power real-time pricing, large-scale data pipelines, and machine learning products.

The Price Optimization (PO) team is where insights become decisions. We work with large-scale customer data and market predictions - like demand forecasts - to drive revenue management decisions that actually move the needle.

At our core, we build and maintain the decision-making engine. That means designing the data pipelines that bring customer data in, and running it through an optimization engine that simulates the market - weighing competition, pricing constraints, inventory availability, predictive models, and each client's unique business policies - to generate the best possible price recommendations. As the final step before recommendations reach the client, quality, reliability, and attention to detail aren't just nice to have. They're everything.

You will lead the team through architecture decisions, development, and deployment of mission-critical systems-while growing and mentoring a high-performing team.

Responsibilities:

Manage a team of data scientists and data engineers responsible for building robust, scalable, and high-performance data pipelines and infrastructure.
Design, build, and maintain distributed data processing workflows (batch & streaming).
Drive best practices for data quality, validation, testing, and observability.
Own and evolve data architecture in alignment with business and product goals.
Manage sprint planning, task breakdown, code reviews, and performance feedback for your team.
Contribute hands-on to key development tasks and architecture decisions.
Recruit, mentor, and grow the data science engineering team.

We are looking for Customer Support Engineerin our site in Netanya with a strong data background, who can provide ongoing support to our client while monitoring our production pipeline ensuring we deliver within the agreed SLA . You will be an essential part of the frontline support team responsible for providing prompt and efficient support and service to end-users experiencing technical issues with software. The primary responsibility is to ensure the timely resolution of data-related problems and to facilitate effective communication between end-users and higher-level support teams. The ideal candidate has relevant experience in data engineering. This role involves coordinating with various teams, managing schedules, and ensuring that production processes run smoothly and efficiently.

Work schedule:

The employee will be required to work 5 days a week, spread across the entire week, including Saturday and Sunday, with off-days during weekdays if shifts fall on weekends. The job is shift-based, with shifts occurring both during the day/evening/night and on weekends.

What You'll Do:

You will be responsible for maintaining and monitoring data pipelines that power our data science workflows while developing and maintaining production schedules.
Identify potential production issues and work proactively to resolve them.
Communicate any production delays or issues to relevant stakeholders and customers.
Maintain accurate records of production activities, including schedules, bugs and project status.
Coordinate with various departments (e.g., Data, DE, PO) to ensure timely completion of tasks.
Serve as the frontline support and provide prompt and efficient support to end-users experiencing technical issues with the platform.
Troubleshoot problems and offer effective solutions to maintain optimal user experiences.
Collaborate with other teams, including R&D and product management, to resolve customer issues.

we are transforming our internal infrastructure into a high-velocity, self-service product. This is a senior opportunity to architect the future of our platform, setting the technical standard for how we deliver services while ensuring reliability and developer independence across diverse global markets.

Responsabilities:

Self-service enablement: Build secure, automated pathways that accelerate development while keeping organizational standards intact.
Architectural modularization: Design Kubernetes-native abstractions - controllers and operators - and decouple infrastructure into independent service lifecycles that support rapid iteration.
Everything as code: Treat infrastructure, policy, and configuration with the same SDLC as application software - versioned, reviewed, tested, and CI-gated.
Proactive reliability: Own production through robust observability, clear SLOs, and high operational hygiene; define alerting that fires correctly and lead blameless postmortems that fix the system.

We're looking for a System Architect to own the technical foundation end to end.

What you'll do

Own the architecture - a scalable, cloud-native system from whiteboard to production on a greenfield Azure environment you'll stand up yourself (Kubernetes-native, Everything-as-Code, GitOps, CI/CD from day one).
Work directly with our Chief AI Officer to translate complex decisioning methodologies into clean,self-service products a customer can run without a data-science team.
Lead the coding. Set the standards, make the hard calls, and write the core code yourself. You'll guide the engineers shoulder-to-shoulder - leadership by example, not by org chart, without managing them directly.
Stand up a fully agentic SDLC - building the product withAI coding agents end to end, so a small team ships like a large one.
Design the integration layer so the product plugs into each host's native agent and data surfaces - customers adopt it without leaving the tools they already use.

We are seeking an MLOps Engineer to help us grow our technical team's capabilities. The ideal candidate has relevant experience in data engineering, preferably within the AI field. Aviation industry experience would be a great addition.

You will be responsible for building and maintaining models and data pipelines that power our data science workflows. You'll play a crucial role in ensuring the accuracy, consistency, and efficiency of the data we use for model training and inference. This involves working with both structured and unstructured data from various sources, leveraging your expertise in data engineering and machine learning to create a robust and scalable system.

We are looking for an MLOps Team Leadto drive the development of an internal machine learning platform for a group of ML teams. This is a hands-on leadership role: you will guide a small team of MLOps engineers that builds the automation and infrastructure powering our research (R&D) workflows and runs our pipelines to production. You will take ownership of end-to-end initiatives and drive the team toward critical infrastructure and model-lifecycle milestones, while staying close enough to the code to set technical direction and raise the bar by example.

You will be responsible for building and maintaining the models and data pipelines behind our data science workflows, ensuring the accuracy, consistency, and efficiency of the data used for training and inference, working across structured and unstructured data from many sources on a large-scale, distributed platform.

Responsibilities:

Lead, mentor, and grow a team of MLOps engineers, owning delivery and technical quality.
Take end-to-end ownership of infrastructure and pipeline initiatives across the LMM group, from design through production.
Stay hands-on: contribute to design and code, review work, and set engineering standards.
Drive the team through critical milestones in ML model-lifecycle and infrastructure ownership.
Partner with R&D and other stakeholders to translate research needs into robust, scalable systems.
Help evolve the platform, including our ongoing migration from Dask to Ray.

We are seeking a talented and self-driven experienced Data Scientist to help advance our machine learning capabilities.This is a key role for someone passionate about leveraging machine learning to solve complex, real-world problems and deliver measurable business impact.

Responsibilities:

Develop and implement state-of-the-art econometric and machine learning models for demand forecasting.
Conduct research and experimentation to evaluate novel approaches for improving accuracy, robustness, and scalability.
Collaborate with cross-functional teams (including product, data engineering, MLOPS and Platform) to deploy ML systems in production.
Clearly communicate complex technical findings to non-technical stakeholders, including product leaders and executives.