Senior AI/ML Consultant - Red Hat OpenShift AI

About the role

The Red Hat Consulting team is looking for a Senior Consultant in Singapore to support the ASEAN region. You will promote the adoption of Red Hat's solutions at key enterprise accounts, government agencies and service provider partners, define requirements and deliver solutions from the Red Hat portfolio, and lead medium to large project teams. You will act as a customer advocate, earning the trust of customer Project, Development and Operations teams.

Key responsibilities

• Design and implement Red Hat OpenShift AI platform architecture and deploy and manage AI workloads on Red Hat OpenShift container platforms.
• Design and implement enterprise Agentic AI solutions using multi-agent frameworks, workflow orchestration, planning, memory, tool integration and AI agent governance.
• Deliver discovery, analysis and design workshops, architecture reviews and knowledge transfer to shape customer architecture decisions.
• Support pre-sales activities such as scoping, technical proposals, solution demos and proofs of concept.
• Mentor junior consultants and customer engineers, and coach others while building customer relationships.
• Contribute to Red Hat's methodologies, playbooks, reference architectures and shared consulting assets, and stay current with emerging AI, Kubernetes and open-source trends.

Required profile

• 10+ years of experience in consulting, solution architecture or technical advisory roles.
• Proven leadership in AI, data and platform modernization initiatives, with the ability to define technical strategy and deliver enterprise-scale transformations.
• Strong customer-facing skills, including leading technical workshops and advising customers on enterprise AI adoption.
• Experience contributing to open-source AI/ML communities is highly desirable.

Required skills

• Strong MLOps expertise, including model training, fine-tuning, deployment, monitoring, governance and lifecycle management.
• Generative AI with LLMs, RAG, vector databases, embedding models and knowledge graphs; hands-on Agentic AI frameworks such as LangGraph, CrewAI, AutoGen or Semantic Kernel.
• Model Context Protocol (MCP) and secure, governed AI solutions with guardrails and responsible AI practices.
• Enterprise AI platforms such as Red Hat AI, Red Hat OpenShift AI, NVIDIA AI Enterprise, Kubeflow, MLflow, KServe or equivalents (highly desirable).
• GPU infrastructure, AI accelerators, workload scheduling, hybrid and multi-cloud integration, and data engineering principles.