AI Specialist Solution Architect, Southeast Asia (Singapore)

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…