AI Specialist Solution Architect, Southeast Asia (Singapore) at Red River

Singapore, , Singapore -

Full Time

Start Date

Immediate

Expiry Date

10 Jun, 26

Salary

0.0

Posted On

12 Mar, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

AI Architecture, MLOps, Deep Learning, Generative AI, Large Language Models, Prompt Engineering, Python, Data Science Lifecycle, Distributed Training, High-Scale Inference, Red Hat OpenShift AI, RHEL AI, vLLM, Retrieval-Augmented Generation, Ansible, PyTorch

Industry

IT Services and IT Consulting

Description

The Red Hat APAC Technology Sales team is looking for an AI Specialist Solution Architect (SSA) to join us in Singapore. In this role, you will be the "technical center of gravity" for the execution of Go-To-Market (GTM) strategies related to Red Hat AI, primarily with Singapore-based public sector, enterprise and commercial accounts. You will play a pivotal role in removing organizational, technical, and competitive barriers to adoption within key accounts. Your mission is to establish Red Hat as the preferred AI platform for enterprise workloads, helping customers leverage their proprietary data to gain a sustainable competitive advantage. You will bridge the gap between business value and deep technical implementation, working closely with data scientists, developers, and IT leadership. What you will do Architect and Design: Plan, design, and execute end-to-end AI systems, including data pipelines, ML pipelines, and distributed training/serving architectures. Technical Sales Support: Provide expert deep-technical support for GTM strategies, facilitating pre and post-sales activities such as technical deep dives, Proofs of Concept (PoCs), "bake-offs," and hackathons. Stakeholder Engagement: Confidently articulate the Red Hat AI value proposition to a range of stakeholders, from C-level executives to Data Science and DevOps teams. Operationalize AI: Implement best practices in AI systems design and MLOps to move customer projects from experimentation to production-ready environments. Product Advocacy: Act as a bi-directional conduit between the field and Red Hat’s AI Business Unit and Engineering teams, providing market feedback and testing cutting-edge features. Community Outreach: Execute advocacy activities including conference speaking, technical blog posts, and participation in volunteer tech communities. What you will bring SME Status: 15+ years of enterprise architecture experience, with 7+ years of hands-on experience in Machine Learning Use Case Development and Machine Learning Operations (MLOps) in enterprise environments. Deep Learning Expertise: Strong foundational and applied knowledge of Deep Learning architectures, including CNNs, RNNs, and LSTMs, with the ability to explain their trade-offs in real-world applications. Gen AI Architect: Proven track record of designing and deploying Generative AI solution architectures, specifically focusing on Large Language Models (LLMs), prompt engineering, and fine-tuning strategies. Technical Depth: Proficient in statistical programming languages (primarily Python) and the end-to-end data science lifecycle. Architecture Excellence: Proven ability to architect and explain complex data/ML pipelines, including distributed training and high-scale inference, to technical and non-technical audiences. Communication: Exceptional presentation skills with the ability to lead business-value sessions and technical workshops for C-level executives and engineering teams. Education: Degree in Computer Science, Mathematics, or a related technical field is preferred. Language: English language fluency required, Mandarin fluency strongly preferred Technical Scope: The Red Hat AI Portfolio You will leverage your technical depth to sell the value proposition of the 2026 Red Hat AI stack: Red Hat AI Enterprise: Positioning the integrated platform for building, developing, and deploying AI-powered applications. You will sell the value of a unified lifecycle for both platform and AI engineers to increase operational efficiency. Red Hat AI Inference Server: Leading the "production-first" conversation by showcasing high-performance, cost-effective inference. You will demonstrate how vLLM and llm-d technologies maximize throughput and minimize latency across any accelerator (NVIDIA, AMD, Intel, Google TPU and more). Open Hybrid Cloud AI: Selling the "run anywhere" advantage by demonstrating how Red Hat AI provides a consistent operational model across bare metal, virtual machines, public clouds, and edge locations. Agentic AI & Orchestration: Positioning Red Hat as the stable foundation for autonomous, goal-oriented AI workflows that integrate with existing enterprise APIs and data sources. Candidates must demonstrate expertise in the following Red Hat-specific and open-source technical domains: Platform Mastery: Hands-on experience with Red Hat OpenShift AI (RHOAI) and Red Hat Enterprise Linux AI (RHEL AI) as the foundation for the AI lifecycle. Model Ecosystem: Familiarity with the Granite open-source model family and the InstructLab methodology for model alignment and tuning. AI Serving & Runtimes: Deep understanding of inference optimization using vLLM, and model serving frameworks like KServe and ModelMesh. Modern AI Patterns: Practical experience implementing Retrieval-Augmented Generation (RAG), Agentic workflows, and vector databases. Infrastructure as Code (IaC): Experience using Ansible and CI/CD solutions to automate MLOps and LLMOps workflows. OSS Frameworks: Advanced knowledge of PyTorch, TensorFlow, and the Jupyter ecosystem. Success Measures (Key Performance Indicators): Technical Validation Excellence: Achieve high success rates in technical Proofs of Concept (PoCs) and pilot programs, ensuring solutions meet or exceed customer requirements for production readiness. Enterprise AI Adoption: Successfully guide key accounts through the transition from experimental AI models to integrated, scalable production environments. Strategic Pipeline Contribution: Actively influence the growth and quality of the AI opportunity pipeline through deep technical discovery and architectural leadership. Ecosystem Scaling: Effectively scale technical expertise across the region by enabling partners and internal teams on modern AI/ML architectural patterns. Thought Leadership: Establish a strong presence in the regional technical community through high-quality technical content, architectural blueprints, and speaking engagements at industry events. Strategic Feedback Loop: Provide consistent, high-impact technical insights from the field to influence global product direction and address regional market needs. 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. 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. We’re the world’s leading provider of enterprise open source solutions—including Linux, cloud, container, and Kubernetes. We deliver hardened solutions that make it easier for enterprises to work across platforms and environments, from the core datacenter to the network edge. At Red Hat, our commitment to open source extends beyond technology into virtually everything we do. We collaborate and share ideas, create inclusive communities, and welcome diverse perspectives from all Red Hatters, no matter their role. It’s what makes us who we are. Some of the most knowledgeable and passionate people in the technology industry work here. Whether we’re building software, championing our products, or training new associates, we’re collaborating openly to make a difference in the world of open source and beyond.

Responsibilities

The specialist will architect and design end-to-end AI systems, including data pipelines and ML architectures, while providing expert technical support for Go-To-Market strategies through pre-sales activities like PoCs and hackathons. They must also operationalize AI by implementing MLOps best practices to move projects from experimentation to production and act as a conduit for field feedback to engineering teams.