דרושים Ansible Technical Support Engineer ב-Red Hat

What you will do

• Architect and lead implementation of new features and solutions for RHOAI

• Innovate in the MLOps domain by participating in upstream communities

• Provide technical vision and leadership on critical and high impact projects

• Ensure non-functional requirements including security, resiliency, and maintainability are met

• Write unit and integration tests and work with quality engineers to ensure product quality

• Use CI/CD best practices to deliver solutions as productization efforts into RHOAI

• Contribute to a culture of continuous improvement by sharing recommendations and technical knowledge with team members

• Collaborate with product management, other engineering and cross-functional teams to analyze and clarify business requirements

• Communicate effectively to stakeholders and team members to ensure proper visibility of development efforts

• Give thoughtful and prompt code reviews

• Represent RHOAI in external engagements including industry events, customer meetings, and open source communities

• Mentor, influence, and coach a distributed team of engineers

What you will bring

• Advanced experience developing applications in Go or Python, or other language

• Advanced experience in Kubernetes, OpenShift or other cloud-native technologies

• Ability to quickly learn and guide others on using new tools and technologies

• Experience with source code management tools such as Git

• Proven ability to innovate and a passion for staying at the forefront of technology.

• Excellent system understanding and troubleshooting capabilities

• Autonomous work ethic, thriving in a dynamic, fast-paced environment.

• Technical leadership acumen in a global team environment

• Excellent written and verbal communication skills

The following will be considered a plus:

• Master’s degree or higher in computer science, machine learning, or related discipline

• Understanding of how Open Source and Free Software communities work

• Experience with development for public cloud services (AWS, GCE, Azure)

• Experience working with or deploying MLOps platforms

What You Will Do

• Implement new features and solutions for Red Hat AI and Edge products.

• Explore deep code integration into various Red Hat products, ensuring optimal integration between the Red Hat portfolio, hardware accelerators and partners.

• Integrate software that leverages hardware accelerators (e.g., DPUs, GPUs, AIUs) and perform performance analysis and optimization of AI workloads with accelerators.

• Work with major AI and hardware partners such as NVIDIA, AMD, Dell, and others on building joint integrations and products.

• Collaborate closely with UX, UI, QE, and cross-functional teams to deliver a great experience to Red Hat partners and customers.

• Coordinate with team leads, architects, and other engineers on the design and architecture of our offerings.

• Become responsible for the quality of our offerings, participate in peer code reviews and continuous integration (CI), and respond to security threats.

What You Will Bring

• 4+ years of relevant technical experience in software development.

• Advanced experience working in a Linux environment with at least one language like Golang, Rust, Java, C, or C++.

• Expereince with the container orchestration ecosystem like Kubernetes, or Red Hat OpenShift.

• Expereince with microservices architectures and concepts including APIs, versioning, monitoring, etc.

• Experience with AI/ML technologies, including foundational frameworks, large language models (LLMs), Retrieval Augmented Generation (RAG) paradigms, vector databases, and LLM orchestration tools.

• Ability to quickly learn and guide others on using new tools and technologies.

• Proven ability to innovate and a passion for staying at the forefront of technology.

• Excellent system understanding and troubleshooting capabilities.

• Autonomous work ethic, thriving in a dynamic, fast-paced environment.

• Technical leadership acumen in a global team environment.

• Proficient written and verbal communication skills in English.

The Following is Considered a Plus

• Experience with cloud development for public cloud services (AWS, GCE, Azure).

• Familiarity with virtualization, networking, or storage.

• Background in DevOps or site reliability engineering (SRE).

• Experience with hardware accelerators (e.g., GPUs, FPGAs) for AI workloads.

• Recent hands-on experience with distributed computation, either at the end-user or infrastructure provider level.

• Experience with performance analysis tools.

• Experience with Linux kernel development.

What you’ll do

• Lead a team of software engineers to build a platform for AI prototype products, focusing on speed and stability.

• Drive collaboration with AI research, Catalyst, product, and design teams to translate prototypes into production

• Create team practices that prioritize rapid iteration, quality standards, and customer outcomes

• Foster a high-performing, autonomous team culture through coaching, mentorship, and strong technical direction

• Manage timelines, unblock delivery, and ensure work aligns with cross-functional product goals

• Champion DevOps, CI/CD, and security practices for robust AI delivery workflows

• Help define and scale internal tooling and frameworks to accelerate the AI productization lifecycle

What you’ll bring

• 6+ years of software engineering experience, including 4+ years in engineering leadership roles

• Proven track record of shipping production systems at scale

• Strong background in backend development (Python, Go, or similar) and cloud platforms (AWS, GCP, Azure)

• Ability to lead in ambiguous, fast-changing environments and prioritize impact over polish

• Excellent communication skills and cross-functional alignment instincts

• A builder mindset—excited by 0→1 delivery, creative problem solving, and rapid experimentation

Nice to have

• Experience launching AI-driven products or internal platforms from prototype to production

• Hands-on experience with LLMs, generative AI, or machine learning-powered applications

• Familiarity with tools like LangChain, vector databases (Pinecone, Weaviate), or RLHF pipelines

• Background in AI systems observability, evaluation frameworks, or in-product feedback loops

About the Job :

Red Hat is looking for a Salesforce Architect with Salesforce Experience Cloud expertise to join the Core Business Platforms organization. You will be responsible for delivering the technology strategy for Salesforce use.

What will you do?

• Work collaboratively with business and technology stakeholders in defining future-state technology architectures and roadmap that take into account the business goals, priorities and timelines.

• Work with stakeholders to understand potential opportunities and recommend solutions.

• Work with business teams to rapidly test out hypotheses, setup and demo Salesforce functionality, and guide the development team for demos.

• Determine and produce artifacts that will guide technical teams to drive to meaningful business outcomes

• Provide oversight for technical work to ensure platform standards are followed

• Monitor and maintain platform health using KPIs for performance, data quality, technical debt and agility

• Maintain up-to-date documentation of current state architecture, data flows and integrations for Sales and Support applications.

• Acts as technical tier 4 for unresolved inquiries within the purview of the role.

• Collaborates with key stakeholders to ensure regulatory and overall data compliance and adherence to business process controls.

What will you bring?

• 7–10 years of Salesforce Development or advanced admin experience with 3+ years of experience as an application/platform architect, with responsibility for defining target state architecture for solutions on Salesforce

• Certifications: Salesforce Experience Cloud Consultant, Salesforce Certified Technical Architect (CTA) or Salesforce Systems Architect

• Strong working experience implementing Salesforce Experience Cloud for Partner Relationship Management (PRM)

• Extensive experience in implementing and managing complex compensation logic, configuring rewards and incentive rules for partners.

• Experience with Rebate Management and Loyalty management.

• Deep understanding of Sales and Service Cloud

• Demonstrated ability to engage stakeholders, align architectural designs, balance speed with best practices, and provide technical guidance to delivery teams.

• Must have expertise in complex Salesforce Flow design and merging, implementing integrations via Invocable objects, and aligning with BPMN-based business processes.

• Extensive knowledge of Salesforce governor limits

• Expertise in applications development such as: Integration Techniques/Patterns, Data Modeling/Patterns, Security Patterns

• Effective interpersonal skills to influence and socialize the solution designs

• Thorough understanding of the Sales domain.

• Experienced with the principles of agile development methodologies

The following are considered as a plus:

• Design and Building of custom solution or managed packages for Salesforce

• Technical governance oversight of multiple development teamsCRM Analytics (CRMA)iPaaS integration tools such as Workato, Boomi or integration using Kafka

Job Summary

You’ll help accelerate the development of AI prototypes by ensuring seamless platform integration, CI/CD pipelines, and other critical infrastructure to enable high-speed experimentation and iteration.

This role combines technical depth with systems thinking. You’ll lead quality strategy and hands-on validation for AI-powered products, working closely with engineering, product, and AI research to ensure that what we ship is both breakthrough and bulletproof.

What you’ll do

• Champion DevOps, CI/CD, and security practices to streamline the deployment of prototypes and AI features, enabling rapid iteration and testing.

• Translate fast-moving prototypes into testable, reliable features using AI-enhanced validation tools and frameworks

• Mentor senior QA and automation engineers, shaping a high-ownership, high-velocity quality culture within the productization team

• Partner with engineers and AI scientists to define test hooks, observability layers, and validation paths during development—not just after

• Create feedback loops that turn AI-driven insights (logs, usage data, heuristic signals) into continuous quality improvements

What you’ll bring

• 10+ years of experience in software or quality engineering, with deep expertise in building and maintaining production-grade test systems

• Familiarity with modern infrastructure and deployment stacks (e.g., Kubernetes, CI/CD, cloud services)

• Experience designing and running end-to-end test suites that mirror real-world customer behaviors and workflows

• Exceptional collaboration and communication skills—able to align product, engineering, and research around pragmatic quality tradeoffs

• Passion for AI product reliability and ensuring breakthrough features don’t break the user experience

Nice to have

• Experience with AI-enhanced QA tools (e.g., GPT-based test generation, anomaly detection, log synthesis)

• Background in usability engineering or customer workflow validation

• Contributions to internal observability tooling, feedback ingestion systems, or AI regression monitoring frameworks

• Exposure to AI research-to-productization pipelines, including real-time and batch model integrations

Job Summary

You’ll help accelerate the development of AI prototypes by ensuring seamless platform integration, CI/CD pipelines, and other critical infrastructure to enable high-speed experimentation and iteration.

This role combines technical depth with systems thinking. You’ll lead quality strategy and hands-on validation for AI-powered products, working closely with engineering, product, and AI research to ensure that what we ship is both breakthrough and bulletproof.

What you’ll do

• Champion DevOps, CI/CD, and security practices to streamline the deployment of prototypes and AI features, enabling rapid iteration and testing.

• Translate fast-moving prototypes into testable, reliable features using AI-enhanced validation tools and frameworks

• Partner with engineers and AI scientists to define test hooks, observability layers, and validation paths during development—not just after

• Create feedback loops that turn AI-driven insights (logs, usage data, heuristic signals) into continuous quality improvements

What you’ll bring

• 6+ years of experience in software or quality engineering, with deep expertise in building and maintaining production-grade test systems

• experience with automated testing frameworks

• Familiarity with modern infrastructure and deployment stacks (e.g., Kubernetes, CI/CD, cloud services)

• Deep understanding of regression risk, usability validation, and quality assurance best practices in fast-moving, high-visibility environments

• Exceptional collaboration and communication skills—able to align product, engineering, and research around pragmatic quality tradeoffs

• Passion for AI product reliability and ensuring breakthrough features don’t break the user experience

Nice to have

• Exposure to AI research-to-productization pipelines, including real-time and batch model integrations

• Experience with AI-enhanced QA tools (e.g., GPT-based test generation, anomaly detection, log synthesis)

• Background in usability engineering or customer workflow validation

• Contributions to internal observability tooling, feedback ingestion systems, or AI regression monitoring frameworks

• Experience designing and running end-to-end test suites that mirror real-world customer behaviors and workflows

Job Summary

You’ll help accelerate the development of AI prototypes by ensuring seamless platform integration, CI/CD pipelines, and other critical infrastructure to enable high-speed experimentation and iteration.

What you’ll do

• Platform Support and Optimization: Design and maintain scalable, secure, and efficient platforms to support AI Catalyst team initiatives, ensuring smooth integration of AI models and workflows.

• Infrastructure Management: Provide expertise in Kubernetes and cloud platforms (GCP, AWS, Azure) for container orchestration, scalable deployments, and real-time operations.

• Partner with the AI Catalyst team to identify bottlenecks, remove blockers, and optimize workflows for faster delivery of AI prototypes.

• Technical Leadership: Lead the implementation of critical systems (APIs, orchestration, observability, deployment) to ensure speed, reliability, and maintainability.

• Cross-Functional Collaboration: Work closely with engineering, product, and design teams to align technical priorities and drive impactful AI initiatives.

• Mentorship: Guide and mentor engineers, fostering a culture of technical excellence, collaboration, and rapid execution.

• Demonstrate proficiency in Kubernetes for container orchestration and scalable deployments.

• Mentor senior engineers and contribute to a culture of technical excellence, velocity, and pragmatic decision-making

What you’ll bring

• 10+ years of software engineering experience

• Strong background in Python and background in C, C++, Go or Rust.

• Proficiency in RHEL or other Linux distributions.

• Communication Skills: Strong ability to communicate technical tradeoffs and bring clarity to ambiguous situations

• Passion for AI Innovation: Enthusiasm for enabling AI initiatives that drive real-world impact and accelerate prototyping efforts.

• Ability to move fast without compromising quality, thriving in environments where rapid iteration and high ownership are the norm

• PoC Experience: Proven ability to work on and deliver successful Proof of Concepts or initiatives, showcasing the ability to rapidly prototype and validate ideas.

Nice to have

• Experience with cloud platforms such as GCP, AWS, or Azure.

• experience with building and packaging Python projects, package managers (dnf, pip), and build systems (cmake, meson)

• experience in working with upstream projects and Open Source communities.

• Experience in early-stage product incubation or 0→1 product delivery

• Contributions to internal AI platforms, model evaluation frameworks, or observability for AI systems