About the Job The Red Hat AI Customer Adoption and Innovation (CAI) team is looking for a North-America based Forward Deployed AI Engineer to join our rapidly growing AI Business Unit. As inference technologies become more mainstream, our customers are seeking deep expertise in optimization, scalability, and production readiness. In this role, you will act as a bridge between engineering and the customer's environment. You will be deployed to support lighthouse customer engagements, ensuring that Red Hat AI inference products are successfully implemented, tuned, and optimized to meet specific business requirements. We are looking for a hands-on practitioner who understands that technical implementation must serve a business constraint—whether that’s cost, latency, or throughput. You will work directly with customers to design and deploy and optimize complex AI inference solutions, while simultaneously capturing those lessons to enable our wider field teams. You must have significant experience (10+ years) as consultant or technical architect. You must also have a really good understanding of inference and inference optimization, as well as practical and relevant experience. While you will have the support of the wider CAI team to upskill on specific AI technologies, you must bring a strong consulting mindset and deep technical expertise in OpenShift or Kubernetes platform engineering as well as a deep understanding of LLMs, Generative AI, and Inference. This position can be remote, but the candidate has to be located in North America, United States preferred, and willing to travel as required, up to 20% of the time. What you will do - Lead Lighthouse Implementations: Lead the technical delivery for critical, high-profile customer Proofs of Concept (POCs) and production pilots. You will be the primary technical expert hands-on with the customer, helping them navigate the complexities of LLM inference in their specific clusters. - Optimization & Architecture: Provide expert advice on inference sizing, configuration, and resource management. You will guide customers on how to best configure their OpenShift environments to support computationally intensive AI workloads. - Field Enablement & Asset Creation: Enable our field teams by turning lessons from customer engagements into reusable assets. You will develop reference architectures, field manuals, and validated patterns that allow other AI specialists to execute similar engagements independently. - Stakeholder Communication: Translate technical metrics into business value. You will be expected to communicate effectively with both technical teams (DevOps, SREs) and business stakeholders to justify architecture decisions. - Product Feedback Loop: Act as a liaison between the customer and the Product and Engineering teams. You will ensure that real-world feedback regarding platform performance and usability is properly prioritized in the product roadmap. What you will bring - Consulting & Architecture Experience (10+ years’ experience): Proven experience in a technical consulting, professional services, or solutions architect role. You are comfortable leading the delivery of complex technical solutions and managing customer expectations in a post-sales or implementation environment. - Deep OpenShift or Kubernetes Expertise (5+ years’ experience): You possess extensive hands-on experience with OpenShift or Kubernetes. You deeply understand how to deploy, scale, and manage complex workloads, operator lifecycles, and resource quotas in a containerized environment. - Performance & Optimization Mindset: You have a background or strong interest in system performance. You understand concepts regarding latency, throughput, and efficient resource utilization. - Inference background (2+ years’ experience): You should already have familiarity with inference technologies such as Kserve, vLLM, and potentially llm-d. - Functional Python Skills: You are capable of reading and writing Python code to script automation or interact with necessary libraries. - Communication Skills: Excellent written and verbal communication skills in English. You can confidently present to audiences ranging from operations teams to business leadership. Preferred Experience (Bonus Points) - Familiarity with the AI Stack: Experience with tools like llm-compressor, guidellm, etc.. - Networking Knowledge: Understanding of networking concepts (L7/Gateway API) or high-performance computing networking. - Model Tuning Experience: Exposure to post-training techniques such as knowledge distillation, LoRA/QLoRA, or quantization. #LI-DL1 The salary range for this position is $116,270.00 - $191,840.00. Actual offer will be based on your qualifications. Pay Transparency Red Hat determines compensation based on several factors including but not limited to job location, experience, applicable skills and training, external market value, and internal pay equity. Annual salary is one component of Red Hat’s compensation package. This position may also be eligible for bonus, commission, and/or equity. For positions with Remote-US locations, the actual salary range for the position may differ based on location but will be commensurate with job duties and relevant work experience. About Red Hat Red Hat is the world’s leading provider of enterprise open source software solutions, using a community-powered approach to deliver high-performing Linux, cloud, container, and Kubernetes technologies. Spread across 40+ countries, our associates work flexibly across work environments, from in-office, to office-flex, to fully remote, depending on the requirements of their role. Red Hatters are encouraged to bring their best ideas, no matter their title or tenure. We're a leader in open source because of our open and inclusive environment. We hire creative, passionate people ready to contribute their ideas, help solve complex problems, and make an impact. Benefits ● Comprehensive medical, dental, and vision coverage ● Flexible Spending Account - healthcare and dependent care ● Health Savings Account - high deductible medical plan ● Retirement 401(k) with employer match ● Paid time off and holidays ● Paid parental leave plans for all new parents ● Leave benefits including disability, paid family medical leave, and paid military leave ● Additional benefits including employee stock purchase plan, family planning reimbursement, tuition reimbursement, transportation expense account, employee assistance program, and more! Note: These benefits are only applicable to full time, permanent associates at Red Hat located in the United States. Inclusion at Red Hat Red Hat’s culture is built on the open source principles of transparency, collaboration, and inclusion, where the best ideas can come from anywhere and anyone. When this is realized, it empowers people from different backgrounds, perspectives, and experiences to come together to share ideas, challenge the status quo, and drive innovation. Our aspiration is that everyone experiences this culture with equal opportunity and access, and that all voices are not only heard but also celebrated. We hope you will join our celebration, and we welcome and encourage applicants from all the beautiful dimensions that compose our global village. Equal Opportunity Policy (EEO) Red Hat is proud to be an equal opportunity workplace and an affirmative action employer. We review applications for employment without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, ancestry, citizenship, age, veteran status, genetic information, physical or mental disability, medical condition, marital status, or any other basis prohibited by law. Red Hat does not seek or accept unsolicited resumes or CVs from recruitment agencies. We are not responsible for, and will not pay, any fees, commissions, or any other payment related to unsolicited resumes or CVs except as required in a written contract between Red Hat and the recruitment agency or party requesting payment of a fee. Red Hat supports individuals with disabilities and provides reasonable accommodations to job applicants. If you need assistance completing our online job application, email application-assistance@redhat.com. General inquiries, such as those regarding the status of a job application, will not receive a reply.

Role Description We are seeking an MLOps / DevOps Engineer with experience in cloud platforms and machine learning workflows. As part of the Digital Data Science team, you will support the development, automation, and deployment of next-generation ML models and data-driven solutions. Your work will directly impact how machine learning models are built, deployed, and scaled across production environments. You will collaborate closely with Data Scientists and Engineering teams to ensure seamless integration of ML workflows into scalable systems. This role requires a balance of technical skills, problem-solving abilities, and a proactive mindset to deliver efficient and reliable solutions. You will have the opportunity to contribute to high-impact projects for a Fortune 500 client while growing your expertise in modern MLOps practices. Responsibilities - Building tools for automation around ML workflows orchestration and model deployments using CI/CD workflows - Automating infrastructure deployments and tool configurations using Terraform or similar IaC tools - Writing clean, maintainable and testable code using Python, Go or other object-oriented programming languages (e.g., Java) - Troubleshooting and resolving issues related to ML workflow deployments in production environments - Generating and maintaining operational reporting pipelines using tools such as BigQuery, Looker and Tableau - Collaborating closely with Data Scientists to improve developer workflows and ensure seamless integration with tools - Working cross-functionally with global teams to support ML systems and deployments Qualifications - Hands-on experience with Vertex AI or similar platforms (e.g., Amazon SageMaker) - Understanding of ML models, model deployment, monitoring and scaling in production environments - Proficiency in Python (1-3 years of experience) - Basic experience with GCP or AWS - Experience with Kubernetes or other container orchestration platforms - Familiarity with CI/CD pipelines and infrastructure automation - Basic knowledge of SQL and reporting tools (BigQuery, Looker, Tableau) - Understanding of cloud technologies and distributed systems - Familiarity with security best practices in cloud and containerized environments - Ability to write unit tests and integration tests, delivering clean and reusable code - Strong problem-solving and debugging skills Nice to Have - Experience with eCommerce / digital platforms - Exposure to Terraform or other Infrastructure as Code tools - Experience working with ML workflows and data pipelines - Familiarity with GenAI tools to optimize and automate processes - Experience collaborating in cross-functional teams (Data Science + Engineering) Soft Skills - Pragmatism. Ability to prioritize, simplify and focus on delivering results - Proactive mindset. Takes ownership and initiative without needing constant direction - Strong communication skills to collaborate with technical and non-technical stakeholders - Ability to work in a fast-paced, dynamic environment - Attention to detail and strong analytical thinking Education - B.S. or M.S. degree in Computer Science or comparable experience Benefits - We have a global team of amazing individuals working on highly innovative enterprise projects & products. - Our customer base includes Fortune 100 retail and CPG companies, leading store chains, fast growth fintech, and multiple Silicon Valley startups. - What makes Confiz stand out is our focus on processes and culture. - Confiz is ISO 9001:2015 (QMS), ISO 27001:2022 (ISMS), ISO 20000-1:2018 (ITSM) and ISO 14001:2015 (EMS) Certified. - We have a vibrant culture of learning via collaboration and making workplace fun. - People who work with us work with cutting-edge technologies while contributing success to the company as well as to themselves.

Role Description This role sits at the intersection of cutting-edge AI and scalable production systems, focusing on building the infrastructure that powers high-performing, reliable AI applications. You will play a foundational role in shaping how AI systems are evaluated, monitored, and continuously improved in real-world environments. Working in a highly autonomous and distributed team, you’ll contribute to developing intelligent features such as AI agents and copilots while ensuring their quality, speed, and accuracy at scale. The position offers a unique opportunity to influence a growing AI platform from the ground up, combining experimentation with robust engineering practices. If you thrive in fast-paced, async environments and enjoy turning data into actionable insights, this role offers both ownership and impact. - Design and build evaluation frameworks to measure AI system performance, including both offline development metrics and real-time production monitoring - Develop observability tools and dashboards to track system quality, latency, and accuracy over time - Investigate and diagnose performance issues across AI pipelines, identifying root causes such as retrieval, ranking, or prompt design flaws - Experiment with different models, architectures, and agent configurations to optimize system outcomes - Prototype and implement improvements to retrieval-augmented generation (RAG) pipelines, including chunking, retrieval, and re-ranking strategies - Analyze user interactions with AI features to uncover opportunities for enhancement and innovation - Collaborate closely with engineering teams to validate improvements and ensure system reliability - Stay up to date with emerging AI tools, frameworks, and best practices to continuously evolve platform capabilities Qualifications - 6+ years of experience in software engineering, data science, or machine learning, with exposure to domains such as NLP, search, or recommendation systems - Proven experience building and evaluating production-grade AI systems, including LLM-based applications and RAG pipelines - Strong proficiency in Python for experimentation, prototyping, and data analysis - Solid understanding of AI system architecture, including embeddings, retrieval mechanisms, and context management - Experience designing evaluation pipelines, A/B testing frameworks, and performance measurement systems - Ability to build internal tools and infrastructure such as dashboards and data processing pipelines - Willingness to work with and learn additional technologies (e.g., Ruby on Rails) for system integration - Strong analytical mindset with the ability to interpret complex data and communicate insights clearly - Comfortable working in fast-paced, ambiguous environments with high autonomy and accountability - Excellent collaboration and communication skills in a distributed, async team - Full professional fluency in English Requirements - Competitive salary package ranging from $130,000 to $140,000 USD annually, with regular compensation reviews - Equity participation and performance-based bonus opportunities - Fully remote work environment with flexibility to work from anywhere - Generous paid time off (35 days annually) plus sabbatical opportunities after long-term tenure - Comprehensive healthcare coverage for employees and their families (or equivalent reimbursement options) - Parental leave to support growing families - Home office setup allowance - Learning and development stipend for continuous skill growth - Biannual company retreats in international locations - High-trust, outcome-driven culture with strong emphasis on autonomy and impact

• Build agentic applications and workflows using the LLM frameworks • Design and implement tool integrations, function-calling patterns, and orchestration logic • Translate agent specifications and prompt strategies into robust, deployable services • Implement RAG pipelines, including vector store integration, chunking strategies, and retrieval optimization • Own the full lifecycle of agent systems from prototype through production • Build evaluation and observability infrastructure • Collaborate with Data Science on prompt engineering, model behavior tuning, and guardrail enforcement • Work with platform and infrastructure teams to deploy and maintain agent services • Contribute to internal tooling, SDKs, and shared libraries