AI Specialist, Solution Architect

Red Hat

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

Posted 11/5/2026full-timeMelbourne • AustraliaSeniorLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive expertise in designing and implementing AI systems, including Machine Learning Operations (MLOps) and Generative AI architectures. Proven ability to communicate complex technical concepts to diverse audiences and lead strategic initiatives in enterprise environments.

Highest-signal resume keywords

Machine Learning Operations (MLOps)Generative AI Solution ArchitecturesDeep Learning Architectures (CNNs, RNNs, LSTMs)Statistical Programming (Python)Red Hat OpenShift AI (RHOAI)

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine Learning Use Case DevelopmentData/ML Pipeline ArchitectureInference OptimizationRetrieval-Augmented Generation (RAG)Prompt EngineeringFine-Tuning StrategiesStatistical Programming (Python)Deep Learning Frameworks (PyTorch, TensorFlow)Model Serving Frameworks (KServe, ModelMesh)Jupyter Ecosystem

Soft Skills

Exceptional Presentation SkillsTechnical CommunicationLeadership in Technical WorkshopsCommunity AdvocacyInfluencing Product Direction

Tools & Technologies

Red Hat AI Enterprise (RHAIE)Distributed Training ArchitecturesVector DatabasesOpen Source ModelsModel Alignment Techniques

Industry Keywords

Enterprise ArchitectureAI Systems DesignTechnical Thought LeadershipMarket FeedbackC-Level Engagement

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, including technical deep dives, Proofs of Concept, bake-offs, and hackathons
Articulate the Red Hat AI value proposition to C-level executives, data science teams, and DevOps teams
Implement AI systems design and MLOps best practices to move customer projects from experimentation to production-ready environments
Act as a conduit between the field and Red Hat’s AI Business Unit and Engineering teams
Provide market feedback and test cutting-edge features
Execute community advocacy activities, including conference speaking, technical blog posts, and participation in volunteer tech 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
Establish regional technical thought leadership through technical content, architectural blueprints, and industry speaking engagements
Provide field insights to influence global 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 Machine Learning Operations (MLOps) in enterprise environments
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
Proven track record of designing and deploying Generative AI solution architectures, specifically focusing on Large Language Models (LLMs), prompt engineering, and fine-tuning strategies
Proficient in statistical programming languages, primarily Python, and the end-to-end data science lifecycle
Proven ability to architect and explain complex data/ML pipelines, including distributed training and high-scale inference, to technical and non-technical audiences
Exceptional presentation skills with the ability to lead business-value sessions and technical workshops for C-level executives and engineering teams
Degree in Computer Science, Mathematics, or a related technical field is preferred
Hands-on experience with Red Hat OpenShift AI (RHOAI) and Red Hat AI Enterprise (RHAIE)
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 Retrieval-Augmented Generation (RAG), Agentic workflows, and vector databases
Expertise with PyTorch, TensorFlow, and the Jupyter ecosystem

Benefits

Comp & perks

Hybrid work environment
Flexible work environments, including in-office, office-flex, and fully remote options depending on role requirements
Equal opportunity workplace
Reasonable accommodations for job applicants with disabilities