Arbitration Forums is looking to leverage GenAI and Agentic AI solutions to improve the accuracy, relevance, and quality of outputs across various business applications, thereby driving business value for the company and its members.
Requirements
- Advanced programming knowledge, including mastery of programming languages such as Python, and especially AI-centric libraries like TensorFlow, PyTorch, and Keras.
- Working knowledge of Azure AI Foundry and ML Studio.
- Working knowledge of low code solutions, as well as tool integration and multi-agent collaboration.
- Cloud computing and knowledge for deploying and managing AI applications on cloud platforms like AWS, Google Cloud, or Microsoft Azure. Deep understanding of containerization technologies like Docker and orchestration tools like Kubernetes for scaling AI solutions.
- Experience combining design patterns like Tool Use, Reflection, and Planning for robust and solid automation.
- Expertise in generative models such as generative adversarial networks (GANs) and variational autoencoders (VAEs). Ability to design, train, and optimize these models to generate high-quality, creative content.
- Proficiency prompt engineering for generative AI models (GPT-4, DALL-E, etc.) and experience creating and integrating RAG capabilities.
Responsibilities
- Design, develop, and refine prompts for LLMs to ensure high-quality outputs for specific use cases.
- Employ techniques to guide and enhance model responses, ensuring that the AI interactions are effective and efficient.
- Develop effective AI interactions through proficient programming and utilization of playgrounds, including the implementation and manipulation of complex algorithms fundamental to developing generative AI models.
- Articulate, design, develop, and implement Agentic AI solutions, following an established development process that is inclusive of the agentic solution lifecycle.
- Conduct A/B testing of prompt variants and automation solutions, analyzing model behaviors and agentic paths, including deviations and degradations.
- Stay up to date with advancements in LLM and RAG capabilities, prompt engineering, and Agentic AI best practices.
- Partner with the MLOps Engineer and other stakeholders to establish and implement observability and monitoring frameworks to adequately and timely identify degradations and potential ethical/bias issues.
Other
- Adheres to AF Policy and Procedures and the AF IPAAL Values and TRI Model
- Acts as a role model within and outside AF.
- Collaborate with cross-functional teams to ensure that the solutions are aligned with the business requirements and objectives.
- Collaborate with product managers, data scientists, ML engineers, and business stakeholders to integrate GenAI and AI solutions into business processes, products, or workflows.
- Work closely with IT, product architecture, data engineers, data analysts, data scientists, and business stakeholders to understand needs, data requirements, and implement solutions.
