AI Specialist Solution Architect, ANZ

Red Hat

. Plan, design, and execute end-to-end AI systems, including data pipelines, ML pipelines, and distributed training/serving architectures .

Posted 10/5/2026full-timeSydney • AustraliaSeniorLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive expertise in designing and implementing AI systems, including MLOps best practices and deep learning architectures. Proficient in guiding enterprise-level AI projects from experimentation to production while effectively communicating technical concepts to diverse audiences.

Highest-signal resume keywords

Enterprise Architecture ExperienceMachine Learning Use Case DevelopmentDeep Learning ArchitecturesGenerative AI Solution ArchitecturesRed Hat OpenShift AI

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine Learning Use Case DevelopmentMLOpsDeep Learning ArchitecturesGenerative AI Solution ArchitecturesStatistical Programming LanguagesData/ML PipelinesInference OptimizationPyTorchTensorFlowJupyter Ecosystem

Soft Skills

Exceptional Presentation Skills

Tools & Technologies

Red Hat OpenShift AIRed Hat AI EnterpriseKServeModelMeshVector Databases

Industry Keywords

AI SystemsMLOps Best PracticesC-Level ExecutivesTechnical WorkshopsOpen Source Models

Tech Stack

Tools & technologies

Open SourceOpenShiftPythonPyTorchTensorflow

About the role

Key responsibilities & impact

Plan, design, and execute end-to-end AI systems, including data pipelines, ML pipelines, and distributed training/serving architectures
Provide deep technical support for go-to-market strategies and pre-/post-sales activities, including technical deep dives, PoCs, bake-offs, and hackathons
Articulate Red Hat AI’s value proposition to C-level executives, data science teams, and DevOps teams
Implement AI systems design and MLOps best practices to move projects from experimentation to production
Advocate for Red Hat AI products and relay market feedback to the AI Business Unit and Engineering teams
Conduct conference speaking, write technical blog posts, and participate in volunteer technology communities
Guide key accounts from experimental AI models to integrated, scalable production environments
Influence the AI opportunity pipeline through technical discovery and architectural leadership
Enable partners and internal teams on modern AI/ML architectural patterns
Produce technical content and architectural blueprints and speak at industry events
Provide field insights to influence product direction and address regional market needs

Requirements

What you’ll need

15+ years of enterprise architecture experience
7+ years of hands-on experience in Machine Learning Use Case Development and MLOps in enterprise environments
Strong foundational and applied knowledge of Deep Learning architectures, including CNNs, RNNs, and LSTMs
Proven track record designing and deploying Generative AI solution architectures focused on LLMs, prompt engineering, and fine-tuning strategies
Proficiency in statistical programming languages, primarily Python, and the end-to-end data science lifecycle
Ability to architect and explain complex data/ML pipelines, including distributed training and high-scale inference
Exceptional presentation skills for business-value sessions and technical workshops
Hands-on experience with Red Hat OpenShift AI and Red Hat AI Enterprise
Familiarity with open source/open weights models, model alignment, fine-tuning practices, LLMOps, and quantization techniques
Deep understanding of inference optimization using vLLM and model serving frameworks such as KServe and ModelMesh
Practical experience implementing RAG, agentic workflows, and vector databases
Expertise with PyTorch, TensorFlow, and the Jupyter ecosystem
Degree in Computer Science, Mathematics, or a related technical field is preferred

Benefits

Comp & perks

Flexible work environments, from in-office to office-flex to fully remote, depending on role requirements
Reasonable accommodations for job applicants with disabilities