AI Specialist Solution Architect, ANZ

Red Hat; Melbourne; In loco; AUD 210.000 - 320.000; Tempo pieno

Descrizione del lavoro

Red Hat APAC Technology Sales is seeking an AI Specialist Solution Architect to lead AI strategy in Australia (Melbourne) and ANZ. You will drive GTM execution, architect scalable AI systems, and serve as the technical centerpiece bridging business value and implementation.

You will collaborate with data scientists, developers, and IT leadership to remove barriers and accelerate adoption of Red Hat AI across public sector, enterprise, and telco accounts.

Competenze

• 15+ years enterprise architecture experience.
• 7+ years hands-on ML use case development and MLOps in enterprises.
• Proven Gen AI solution design for LLMs, prompt engineering and fine-tuning.
• Proficient in Python and end-to-end data science lifecycle.
• Able to architect and explain complex data/ML pipelines to technical and non-technical audiences.
• Degree in CS/Math or related field preferred.

Mansioni

• Architect and design end-to-end AI systems including data and ML pipelines.
• Provide deep-technical support for GTM strategies, PoCs, bake-offs and hackathons.
• Engage with stakeholders from C-level to DevOps to articulate AI value.
• Operationalize AI by applying MLOps best practices for production-ready deployments.
• Advocate AI features with Red Hat AI BU and Engineering teams.
• Lead technical blog posts and conference talks to build community around AI.

Conoscenze

AI architecture

MLOps

Generative AI

Python

Stakeholder communication

Formazione

Bachelor's degree in Computer Science

Strumenti

Red Hat OpenShift AI

KServe

ModelMesh

vLLM

Descrizione del lavoro

Role Overview:

The Red Hat APAC Technology Sales team is looking for an AI Specialist Solution Architect (SSA) to join us in Australia (Melbourne) to cover customer engagements across ANZ. In this role, you will be the "technical center of gravity" for the execution of Go-To-Market (GTM) strategies related to RedHat AI across public sector, enterprise, commercial and telco accounts.

You will play a pivotal role in removing organizational, technical, and competitive barriers to adoption within key accounts. Your mission is to establish RedHat 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 RedHat 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 RedHat'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.

Technical Scope: The RedHat AI Portfolio

You will leverage your technical depth to sell the value proposition of the 2026 RedHat AI stack:

• RedHat 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.
• RedHat 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 RedHat AI provides a consistent operational model across bare metal, virtual machines, public clouds, and edge locations.
• Agentic AI & Orchestration: Positioning RedHat 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 RedHat-specific and open-source technical domains:

• Platform Mastery: Hands-on experience with RedHat OpenShift AI (RHOAI) and RedHat AI Enterprise (RHAIE) as the foundation for the AI lifecycle.
• Model Ecosystem: Familiarity with open source / open weights models as well as model alignment and fine-tuning practices (LLMOps) and quantization techniques.
• 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.
• OSS Frameworks: Expertise with PyTorch, TensorFlow, and the Jupyter ecosystem.

Success Measures (Key Performance Indicators) Technic