דרושים » תוכנה » Data/ML Product Owner

The AI, Data & Research unit is at the forefront of our companys innovation, building data-driven, ML-powered, and intelligent security solutions. We are looking for a passionate MLOps Engineer to join our team of seasoned ML engineers.
You will play a critical role in building a multi-tenant PaaS for ML pipelines and inference, ensuring scalability, reliability, and security. You will take ownership of critical platform components, drive best practices, and mentor other engineers.
Design, build, and maintain infrastructure-as-code using Python and AWS services for deployment.
Architect, build, and manage Docker-based services.
Lead the design and implementation of solutions using AWS services such as SageMaker, Lambda, Step Functions, SageMaker Pipelines, Batch Transform, and Real-Time Endpoints.
Enhance and maintain CI/CD pipelines (Jenkins and shared libraries).
Ensure multi-tenant security and tenant isolation across the platform.
Define and implement observability and monitoring practices with Datadog and other tools.
Collaborate closely with Data Scientists, Data engineers, MLEs, Product Managers, and other engineering teams to integrate ML workflows.
Mentor junior engineers and promote engineering best practices.

We are seeking a hands-on Tech Lead to be the first technical hire for our newly formed ML team. This is a unique ground-floor opportunity to build something from scratch and have direct influence on the technical direction of our ML capabilities. This role combines deep technical expertise with foundational leadership, requiring someone who can architect solutions, establish technical standards, and lay the groundwork for future team growth. As our founding ML tech lead, you'll have the opportunity to shape our technology choices, define our technical practices, and build the fundamental skill set that will guide the team as we scale.
Hybrid
Full-time
What youll do: 
Shape the technical foundation: As our first ML hands on hire, choose and establish the team's core technology stack, frameworks, and development practices together with the ML leader
Build from the ground up: Design and implement our initial ML infrastructure, data pipelines, and model deployment frameworks
Define technical standards: Establish coding standards, best practices, and processes that will guide future team members
Hands-on development: Lead by example through direct development and deployment of ML models and data science solutions
Partner with stakeholders: Work closely with business teams to identify initial ML opportunities and build foundational proof-of-concept models
Establish MLOps practices: Set up our model development, testing, deployment, and monitoring workflows from scratch
Recruit and onboard: As the team grows, play a key role in technical interviews and help onboard new ML engineers and data scientists
Create team culture: Help establish the team's technical culture, learning practices, and collaboration methods
Technology evaluation: Research and select the tools, platforms, and technologies that will form our ML tech stack
Document and share knowledge: Create foundational documentation and knowledge-sharing practices for the growing team.

We are seeking a Technical Program Manager (TPM) to join our centralized Quality & Automation infra-Group. This team is at the forefront of a company-wide transformation toward modern, scalable, and data-driven quality engineering practices tailored to SaaS. The TPM will lead key initiatives focused on unifying tools, frameworks, and processes and improve testability across a large-scale distributed organization.
This role is ideal for someone who thrives in navigating complex ecosystems, aligning stakeholders, and bridging technical and organizational priorities with clarity and impact.
Key Responsibilities
Partner with cross-functional teams (R&D, QA, DevOps, Product) to gather requirements and identify quality gaps across the development lifecycle.
Translate product quality strategy into concrete execution plans, including scoping MVPs and managing proof-of-concepts (POCs) for quality and test automation tooling.
Collaborate closely with quality infrastructure architects and automation infra development teams to design, prioritize and drive adoption of shared quality platforms.
Facilitate alignment and cross-team cooperation for technical initiativesOKRs, roadmaps, reporting, and stakeholder communication.
Drive implementation and technical validation of quality and automation tools within various engineering teams.
Use data analysis to track progress, identify patterns, and surface insights that inform quality strategy and operational decisions.