Sr. AI Partner Solution Architect, Anz Partner Team

Amazon Amazon · Big Tech · NSW, Australia +1 · Solutions Architect

This role focuses on accelerating Generative AI, Agentic AI, and ML adoption by acting as a trusted technical advisor to AWS partners. The Senior AI Partner Solutions Architect will design and implement scalable AI/ML solutions using AWS services, guide partners on architectural patterns including agentic workflows and RAG, and deliver enablement programs. The ideal candidate has extensive hands-on experience with production AI systems and influencing technical and business decision-makers.

What you'd actually do

  1. Own the technical AI/ML relationship with our most strategic partners in ANZ, acting as their trusted advisor on Generative AI, Agentic AI, and ML adoption, architecture, and go-to-market strategy.
  2. Architect scalable AI/ML solutions with partners using AWS services, guiding designs from ideation through production deployment including agentic workflows, RAG implementations, and multi-agent orchestration patterns.
  3. Design and deliver AI-focused enablement programs: workshops, reference architectures, code samples, and best-practice guides, that uplift partner AI capability at scale.
  4. Contribute thought leadership through whitepapers, blogs, event presentations, and hands-on deep dives that help partners and customers innovate with AI and ML technologies.
  5. Collaborate cross-functionally with Account SAs, AI Specialists, Sales, Business Development, and AWS service teams to accelerate customer adoption of partner AI solutions.

Skills

Required

  • 10+ years of specific technology domain areas (e.g. software development, cloud computing, systems engineering, infrastructure, security, networking, data & analytics) experience
  • 5+ years of design and implementation of production AI/ML systems, including recent experience with Generative AI, large language models, or agentic AI solutions
  • Experience implementing AI solutions that may include integration of LLMs/multi-modal foundation models, RAG, vector databases, agentic workflows, prompt engineering, and MLOps
  • Experience communicating with and presenting to technical and non-technical executive audiences, including C-level stakeholders

Nice to have

  • Experience with agentic AI solution frameworks (e.g., LangGraph, CrewAI, ADK) and complex agentic patterns (e.g., ReAct, self-reflection, hierarchical delegation)
  • Hands-on experience with AWS AI ecosystem including Amazon Bedrock, Amazon Bedrock AgentCore, Amazon Quick, Kiro, Amazon SageMaker, and AWS Strands Agents SDK.
  • Working familiarity with AI developer tooling from AWS partners such as Anthropic Claude Code, OpenAI Codex, or similar AI-powered development platforms.
  • Hands-on-experience on multi-agent orchestration patterns, Model Context Protocol (MCP), Agent-to-Agent (A2A) protocols, and memory management strategies.
  • Experience in running and fine-tuning large and small language models using techniques such as LoRA/QLoRA, instruction tuning, and RLHF.
  • Working knowledge of "Large Language Models (LLMs)-native" metrics (e.g., tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.

What the JD emphasized

  • production AI systems
  • agentic AI
  • Generative AI
  • ML adoption
  • agentic workflows
  • RAG implementations
  • multi-agent orchestration patterns

Other signals

  • Generative AI
  • Agentic AI
  • ML adoption
  • production AI systems
  • AWS services