1 day ago
Sydney, Australia or Melbourne, AustraliaStaff+
Responsibilities
- Architect and design end-to-end AI systems, data pipelines, ML pipelines, distributed training architectures, and serving architectures.
- Provide deep technical support for go-to-market strategies and pre- and post-sales customer engagements, including technical deep dives, proofs of concept, bake-offs, and hackathons.
- Present Red Hat AI’s value proposition to C-level executives, data science teams, developers, DevOps teams, and IT leadership.
- Implement AI systems design and MLOps best practices to move customer projects from experimentation into production-ready environments.
- Demonstrate Red Hat AI Enterprise, Red Hat AI Inference Server, Open Hybrid Cloud AI, and agentic AI orchestration capabilities.
- Advocate for customer and market needs with Red Hat’s AI Business Unit and Engineering teams, including testing emerging features.
- Enable partners and internal teams on modern AI and ML architecture patterns across the region.
- Contribute to conferences, technical blog posts, industry events, architectural blueprints, and volunteer technology communities.
Requirements
- 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, and their real-world trade-offs.
- Proven experience designing and deploying generative AI architectures involving LLMs, prompt engineering, and fine-tuning strategies.
- Proficiency in Python and the end-to-end data science lifecycle.
- Ability to architect and explain complex data and ML pipelines, distributed training, and high-scale inference to technical and non-technical audiences.
- Exceptional presentation skills and experience leading business-value sessions and technical workshops for executives and engineering teams.
- Hands-on experience with Red Hat OpenShift AI and Red Hat AI Enterprise.
- Familiarity with open-source and open-weights models, model alignment, fine-tuning, LLMOps, and quantization.
- Deep understanding of inference optimization with vLLM and model serving frameworks such as KServe and ModelMesh.
- Practical experience implementing Retrieval-Augmented Generation, agentic workflows, and vector databases.
- Expertise with PyTorch, TensorFlow, and the Jupyter ecosystem.
- A degree in Computer Science, Mathematics, or a related technical field is preferred.
Benefits
- The role is based in Melbourne, Australia and covers customer engagements across ANZ.
- Red Hat supports flexible work environments ranging from in-office and office-flex to fully remote depending on role requirements.
- Red Hat provides reasonable accommodations to applicants with disabilities.
- Red Hat is an equal opportunity and affirmative action employer.
Tech Stack
Categories
Solutions Engineering
About Red Hat
Red Hat builds enterprise open-source software, including Red Hat Enterprise Linux, OpenShift for Kubernetes, and Ansible automation, sold via subscriptions with support, training, and consulting. It serves large enterprises and public-sector organizations running hybrid and multi-cloud infrastructure and modern application platforms. Founded in 1993 and headquartered in Raleigh, North Carolina, Red Hat operates as an independent subsidiary of IBM.