Technical Solution Leader

1 week ago

Sydney, New South Wales, Australia beBeeAgent Full time $180,000 - $250,000
Solution Architect

This is a high-profile role for experts in shaping and leading pre-sales engagements, crafting solution visions, and designing robust architectures addressing complex challenges in various sectors.

Key Responsibilities:
  • Craft solutions to address intricate business problems in regulated environments.
  • Lead workshops, discovery sessions, responses to requests for information, business cases, and architecture deep-dives.
  • Design demonstrators and proofs of concept to showcase capabilities and accelerate buying confidence.
  • Translate scope into delivery-ready programs with seamless handover to delivery teams.
  • Advocate best practices and responsible AI, embedding security, privacy, governance, and ethical considerations from day one.
  • Architect, prototype, and deliver production-grade agentic architectures.
  • Build integrations with enterprise systems, APIs, and workflows ensuring scalability and resilience.
  • Deploy, monitor, and continuously improve AI agents in real-world client environments.
  • Mentor consultants and engineers, elevating capability across the region and contributing to global communities of practice.
Essential Skills:
  • Proven track record of building and deploying AI agents or agentic architectures in production.
  • Ability to lead pre-sales engagements – shaping solution visions, running workshops, responding to requests for proposals/information, and designing architectures that win client trust.
  • Strong grasp of agentic AI frameworks (e.g. LangChain, LlamaIndex, AutoGen) and orchestration patterns.
  • Ability to explain and simplify complex AI concepts to technical teams and senior executives.
  • Solid software engineering fundamentals including continuous integration/continuous deployment, observability, testing, and scalable system design.
Desirable Skills:
  • Experience designing multi-agent systems or autonomous workflows.
  • Knowledge of retrieval-augmented generation patterns, vector databases, and fine-tuning techniques.
  • Hands-on experience with cloud AI platforms (Azure, AWS, GCP) and MLOps practices.
  • Publications, conference speaking, or open-source contributions in the AI/agent space.
  • Ability to mentor and coach teams.

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