AI Solutions Architect

Overview : AI is pivotal to the transformation of the Service and Operations Management space. As Xurrent’s AI Engineer you’ll play a critical role in developing platforms and solutions that transform how work is done in this space. You’ll be responsible for collaborating with other members of the R&D organisation to deliver AI software in the form of data engineering, prompt engineering, algorithms and APIs. You’ll be measured on your ability to produce production ready code and create rapid prototyping opportunities presented to relevant stakeholders. As a senior engineer with the team, you’ll also maintain high code quality and perform code reviews. You’ll also collaborate with DevOps and MLOps to maintain a modern, well architected and well instrumented deployment and testing pipeline.

Responsibilities :

Collaborates with other members within the Research & Development department to determine functional and non-functional requirements for proposed software adjustments or extensions that are related to artificial intelligence and incorporate into A.I. RFCs Assists product management to develop high-level product specifications that are related to artificial intelligence with attention to system integration and feasibility Develops software adjustments and extensions that meets the agreed on specifications and requirements and adheres to coding standards Provide technical guidance and coaching related to artificial intelligence to other members within the Research & Development department Ensure that artificial intelligence software use within the Xurrent services meets all requirements of quality, security, modifiability, extensibility etc. Continuously improve the artificial intelligence software of the Xurrent services by actively participating in its development

Requirements :

Proven ability to develop AI features via LLM Atleast 2-6 years of experience in relevant field Proven ability to develop, deploy, maintain and enhance machine learning models Proven ability to develop and maintain corresponding data systems such as feature stores and data pipelines for AI and machine learning Proven ability to translate product requirements into technical specifications Experience in a production engineering environment