Chief architect – AI Position

Position: Chief architect Role – AI Position

Location: Sydney

Duration: 6 months

Detailed Job Description

• Solution Design & Development
  : Collaborate with stakeholders to turn business challenges into technical solutions underpinned by generative AI, leveraging multi-modal LLMs that encompass voice, text, and vision capabilities, including running models locally.
• Prototyping Tools
  : Utilize tools such as Gradio and Flutter for rapid prototyping and demonstration of AI solutions, focusing on seamless integration of multi-modal inputs and outputs.
• Performance Tuning & Benchmarking
  : Optimize and improve the performance of AI solutions by implementing best practices (re-ranking, indexation). Conduct benchmarking to test and validate the effectiveness and efficiency of AI models and solutions, ensuring they meet required standards for both prototypes and full-scale implementations.
• Cloud Infrastructure
  : Utilize both GCP and AWS cloud stacks for development and deployment, employing Python, Jupyter notebooks, and relevant technologies for managing large-scale multi-modal data.
• Vendor Management
  : Act as a Subject Matter Expert (SME) in vetting and evaluating vendor solutions to ensure alignment with project goals and the integrity of complex AI implementations, ensuring the effectiveness of multi-modal LLM utilization.
• Guardrails and Ethics
  : Maintain an awareness of AI/GenAI ethics, guardrails, and principles, ensuring solutions are developed responsibly and ethically, especially in handling sensitive multi-modal data.
• Agile Development
  : Use Jira for task and project management, ensuring agile methodologies are followed to streamline development processes and enhance collaboration on multi-modal projects.
• Stakeholder Engagement
  : Engage with stakeholders to ensure alignment of technical solutions with business needs, facilitating feedback and iteration throughout the development process, and tailoring multi-modal capabilities to specific user requirements.
• Continuous Learning & Research
  : Stay abreast of the latest developments in AI/GenAI technologies, including advancements in multi-modal LLMs, enhancing solutions through cutting-edge research and innovation.
• Solution Design & Development
  : Collaborate with stakeholders to turn business challenges into technical solutions underpinned by generative AI, leveraging multi-modal LLMs that encompass voice, text, and vision capabilities, including running models locally.
• Prototyping Tools
  : Utilize tools such as Gradio and Flutter for rapid prototyping and demonstration of AI solutions, focusing on seamless integration of multi-modal inputs and outputs.
• Performance Tuning & Benchmarking
  : Optimize and improve the performance of AI solutions by implementing best practices in model tuning. Conduct benchmarking to test and validate the effectiveness and efficiency of AI models and solutions, ensuring they meet required standards for both prototypes and full-scale implementations.
• Cloud Infrastructure
  : Utilize both GCP and AWS cloud stacks for development and deployment, employing Python, Jupyter notebooks, and relevant technologies for managing large-scale multi-modal data.
• Vendor Management
  : Act as a Subject Matter Expert (SME) in vetting and evaluating vendor solutions to ensure alignment with project goals and the integrity of complex AI implementations, ensuring the effectiveness of multi-modal LLM utilization.
• Guardrails and Ethics
  : Maintain an awareness of AI/GenAI ethics, guardrails, and principles, ensuring solutions are developed responsibly and ethically, especially in handling sensitive multi-modal data.
• Agile Development
  : Use Jira for task and project management, ensuring agile methodologies are followed to streamline development processes and enhance collaboration on multi-modal projects.
• Stakeholder Engagement
  : Engage with stakeholders to ensure alignment of technical solutions with business needs, facilitating feedback and iteration throughout the development process, and tailoring multi-modal capabilities to specific user requirements.

• Continuous Learning & Research
  : Stay abreast of the latest developments in AI/GenAI technologies, including advancements in multi-modal LLMs, enhancing solutions through cutting-edge research and innovation.

• Agentic AI familiarity
  is a plus.

• Model tuning is a plus