דרושים » תוכנה » Senior Software Engineer

We are looking for ambitious talents to join our amazing dev team!
The team is responsible for the backend development of multiple SaaS web products (CRM, Commerce, Data Automation) in a highly innovative, technical, and empowering work environment.
Our products are in use by millions of users worldwide, delivering smart and intuitive web experiences that put the end-user in the center.
In this role you will design & develop a challenging high-end SaaS web products for some of the biggest operations in the world, in a great work atmosphere and with highly skilled teammates.
Responsibilities:
Design, build, and maintain efficient, reusable, and reliable code for the development of our API and backend services
Work closely with your team leader and engineering colleagues to implement the best possible solutions and products

Experienced Backend Team Leader? It's time for your next challenge.
Our mission is to create the ultimate CRM Suite for our clients to manage, sell, automate, track & grow their customer database and sales.
Were responsible for crafting innovative, smart & flexible SaaS systems that help companies to scale and grow in challenging environments.
Our solutions are implemented worldwide, in more than 15 countries, delivering outstanding digital experiences for millions of people.
As we continue to grow, were looking for a passionate, fun to work with, Backend Team Leader to join our team and build great products with us.
Responsibilities:
Lead a team of backend developers including hands-on involvement
Responsible for tasks analysis, design, timelines and quality
Work closely with the company CTO and Product Managers
Work closely with other development and QA teams and groups
Responsible for the code standard and code quality of the team
Responsible to enforce the development processes of the team
Report risks or any delays in advance
Committed to team tasks
Support team members

We are looking for a Software Automation Engineer to join our QA team and to take part in the testing process from planning to execution.
Responsibilities:
Responsible for QA processes, testing and release to production of multiple SaaS products.
Build the test plan for the products.
Identify the risks and appropriate test coverage to validate new features as well as regression and develop test plans and cases.
Lead the test execution and preparation for release.
Execute test cases, manually or in an automated fashion, capture and analyze the results, document issues and prepare test reports.
Work with the R&D and Product to deliver high-quality tested SaaS products.
Write test automation for APIs, UI and stress-testing, as an integrated member of an agile R&D team, building a robust application.
Take responsibility for the quality delivery of product releases.
Participate in defining and optimizing the R&D work processes.

We are looking for an experienced and leadership-oriented ATE Software team leader to lead our software ATE development team. In this role, you will manage a team of software engineers, overseeing the design and execution of advanced ATE software for electronic cards and calibration systems used in our cutting-edge AESA radar technology The ideal candidate will combine strong software management skills with a deep understanding of RF systems and hardware-software integration. Key Responsibilities:
* Lead, mentor, and manage a team of ATE software engineers.
* Lead the design and development of automated TEST software for electronic cards and radar calibration.
* Oversee the full software development lifecycle, from requirements definition to deployment on the production line.
* Collaborate closely with Hardware, system, and RF engineers to ensure seamless integration of software with complex testing equipment.
* Drive technical excellence, code quality, and the implementation of best practices within the team.
* Manage project timelines, milestones, and resource allocation to meet delivery goals.

The position is based in Netanya, with occasional travel required to the production facility in Beit She'an.

We are seeking a hands-on technical leader to architect and lead the development of production LLM-based applications. This role will lead a cross-functional team of software engineers and data scientists and will own architecture, delivery, evaluation, observability, and production readiness.



The ideal candidate is a proven software leader who has delivered complex production-grade systems and can align engineering, data science, product, and platform work into one cohesive execution model. The role requires strong software architecture, applied AI judgment, disciplined execution, and people leadership, with a focus on turning ambiguous product goals and exploratory AI work into reliable production software.



Responsibilities:

Lead a cross-functional team of engineers and data scientists developing LLM-based applications.
Translate product and business goals into technical roadmaps, milestones, and deliverable plans.
Own end-to-end delivery from architecture and experimentation through deployment, monitoring, evaluation, and continuous improvement.
Design LLM workflows using RAG, tool calling, structured outputs, agents, deterministic logic, and human-in-the-loop patterns where appropriate.
Establish evaluation-driven development practices for LLM features.
Define quality metrics, regression tests, golden datasets, production feedback loops, and observability standards.
Ensure systems meet production standards for scalability, performance, reliability, security, and maintainability.
Provide technical guidance, architectural oversight, mentorship, and career development for team members.
Manage priorities, scope, risks, and trade-offs across multiple streams of work.
Partner with product, design, DevOps, data engineering, security, and other technical leaders.
Drive continuous improvement in delivery quality, engineering practices, and team effectiveness.


We're looking for an experienced Senior Software Engineer with experience in building AI based products to join our Insights Business Unit. You'll play a crucial role in building AI-powered features that turn our digital shelf and market share data into actionable insights, working alongside a talented and passionate team.
Responsibilities:
End-to-end feature development for AI-powered analytics on rich datasets, from architecture to production.
Mentor teammates and partner with Product, Data Science, and Design to deliver reliable conversational flows.
Design secure, low-latency data retrieval and AI integration pipelines (function calling, agentic workflows, etc) for internal datasets and APIs.
Build and scale integrations to data sources - defining schemas, queries, and caching strategies; continuously improve backend components to deliver a top‑notch user experience with efficient resource trade‑offs.
Establish quality guardrails, automated tests/evaluations, and observability; optimize for scalability, performance, and cost; run A/B tests.
Optimize context engineering and data retrieval strategies - balancing relevance, latency, and token efficiency to deliver accurate AI-powered insights.

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.

WHAT YOU WILL DO

- 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 detail-oriented and collaborative Senior ML Engineer to help support and maintain our machine learning capabilities. This role is ideal for someone who enjoys working closely with production systems, ensuring reliability, scalability, and explainability of models while enabling research teams to deliver impact faster.



Responsibilities



Collaborate with cross-functional teams to ensure ML systems remain robust, explainable, and aligned with business needs.
Monitor and report on ML model performance, reliability, and explainability metrics.
Participate in model retraining procedures, implement automation and optimization of MLOps pipelines.
Extend and scale monitoring pipelines, including support for new features in development.
Investigate, troubleshoot, and resolve issues in production ML workflows (tiered support from initial triage to root-cause analysis with model owners).
Develop and maintain repositories for feature engineering, inference monitoring pipelines, and artifact monitoring tools.
Perform exploratory data analysis (EDA) on historical datasets to identify quality issues and maintain data health.
Implement and oversee production based adjusters across customer deployments.
Evaluate and track critical ML artifacts such as explainability files, coverage metrics, and alignment of features.
Support development and maintenance of internal tools (e.g., interfaces, registries, and feature monitoring frameworks).
Build and maintain static and temporal features, including seasonality, event-based, and price-related features.

Join GenAI team to build and extend the AI assistant embedded across our product suite. The platform is a production LLM agent system that's integrated into multiple host applications, backed by a RAG knowledge base and an evaluation-driven development workflow.

You'll work end-to-end: agent design, tool implementation, retrieval quality, integration into existing product UIs, cloud infrastructure, and evaluation/observability. The platform already ships to customers - you'll extend it, raise its quality bar, and help define where it goes next.

What you'll do

Design and evolve agents - build LLM agents with tool use, routing, and human-in-the-loop flows.
Implement tools and integrations - expose product capabilities to the agent, with multi-tenant context, via internal APIs and MCP servers.
Own retrieval quality - contribute to our RAG pipeline end-to-end: ingestion, embeddings, vector search, and reranking.
Define and evolve host integration contracts - collaborate with host application teams to integrate the assistant into product UIs built on different frontend stacks. You own the shared remote module and the integration API; host teams own their stacks.
Drive evaluation-led development - write evaluators (rule-based, LLM-as-judge, multi-turn), maintain CI eval gates, and use traces and feedback to debug production behavior.
Operate the platform - own deployments, observability, and the performance and cost of LLM-backed services.
Establish engineering practices** for AI-specific work: prompt versioning, eval coverage, testing, and code review.

Were looking for a technical professional, with proven experience for building secure, scalable artificial intelligence systems as well as data-for-AI solutions. In this role you will develop our cutting-edge AI Data platforms used by world-leading brands.
Responsibilities:
Data Pipelines: Clean, preprocess, and analyze large datasets to ensure high-quality inputs and results.
Deployment & Integration: Help deploy AI agents into production environments and integrate them into existing workflows.
Monitoring & Optimization: Evaluate model performance, test for accuracy, and tweak algorithms for continuous improvement.
Documentation: Maintain clear documentation for AI model architectures, data pipelines, and deployment processes
Scale: Build highly scalable solutions that can leverage large data sets effectively