Generative AI Application Engineer

Purple Squirrel Enterprises is recruiting for a well-established SaaS data analytics provider serving the banking industry. The Generative AI Application Engineer will design, build, and ship production generative AI features using LLM APIs, while developing the supporting application, API, SQL, data, evaluation, and monitoring layers. The role also involves collaborating with product management, data engineering, and the AI/Modeling team in a regulated financial-data environment.

Responsibilities:

• Design, build, and ship production generative AI features developed directly against LLM APIs — prompt construction, structured output handling, tool/function calling, retrieval, context management, and failure handling
• Build and maintain the evaluation and regression harnesses that determine whether a generative AI feature is behaving correctly as models, prompts, and data change
• Integrate generative AI components with existing product services, APIs, and data models
• Work with models hosted on Hugging Face and with internally hosted inference engines, including configuration and deployment of the serving layer
• Design, build, and maintain API-based applications and services following the team's software and product life-cycle processes
• Write and tune SQL against relational data models for application state, feature data, evaluation datasets, and product analytics
• Deploy and operate application components across multiple resource types in a hyperscaler environment (VMs, containers, object storage, RDBMS services) — primarily Microsoft Azure
• Contribute to analytics product work that doesn't involve AI/models, when needed — this is not the primary focus, but it happens
• Contribute to model-based analytics such as customer segmentation, churn forecasting, and lifetime value forecasting, working alongside senior members of the AI/Modeling team
• Support internal initiatives that use generative AI, models, and heuristics to increase organizational efficiency
• Work with product management, data engineering, and AI/Modeling teammates to translate requirements into shipped features
• Participate in code review — both giving and receiving
• Communicate progress, technical trade-offs, and blockers clearly to your team and to stakeholders outside it
• Write automated tests appropriate to the work, including evaluation tests for generative AI components and model-evaluation tests for ML components
• Monitor deployed AI applications in production for accuracy, latency, token consumption, and cost; participate in production support as needed
• Follow security, data handling, and change-management practices required when working with sensitive financial data