AI Platform Engineer

TEAM

AI Enablement builds Raisin's internal AI platform, making it easy and safe for teams across engineering, product, and business operations to use AI in their daily work and products.

The team provides governed access to AI models and tools, data security controls, audit logging, and visibility into AI usage, reliability, and costs. It also shapes AI-assisted software development and agentic workflows. Security and compliance are central to the platform due to Raisin's regulated financial services environment.

As an early member of the team, you will help shape how AI is adopted across Raisin.

JOB SUMMARY

Raisin is seeking an AI Platform Engineer to build and operate the internal platform that enables teams to use AI securely and reliably. The role involves developing shared AI services, integrating AI agents with internal systems, creating agentic software development workflows, and improving AI adoption across engineering teams.

You will own projects from initial design through production, collaborate directly with internal users, make technical decisions, and ensure the platform meets security, compliance, reliability, and cost-efficiency requirements.

KEY RESPONSIBILITIES

- Design, build, and operate shared AI platform services, including authentication, model routing, usage limits, guardrails, and audit logging.

- Develop secure integrations that allow AI agents to access internal systems and data with appropriate identities, permissions, and reviews.

- Manage production infrastructure using infrastructure as code, observability, cost monitoring, and incident response.

- Build agentic workflows that support software development, including planning, implementation, code reviews, testing, and releases.

- Create reusable AI tools, agent instructions, skills, and integrations that engineering teams can adopt and extend.

- Help engineering teams implement AI-assisted development practices that improve software quality and delivery speed.

- Collaborate with engineers, product managers, and business teams to identify requirements and develop useful platform capabilities.

- Improve platform adoption through self-service tools, sensible defaults, and clear documentation.

- Track adoption, reliability, costs, and business impact to guide product and technical priorities.

- Lead technical design decisions, evaluate build-versus-buy options, and document architectural choices.

- Collaborate with security, legal, and compliance teams to implement automated platform controls.

- Evaluate AI models based on cost, latency, and output quality.

- Support engineering excellence through design reviews, pairing, and knowledge sharing.

REQUIRED QUALIFICATIONS

- Five or more years of software engineering experience in backend, platform, or infrastructure roles.

- Experience operating production systems used by other teams.

- Background in internal platforms, developer tooling, or shared engineering services.

- Strong programming skills in at least one language, preferably Python; TypeScript and JVM languages are also relevant.

- Solid knowledge of cloud platforms such as AWS, containers, infrastructure as code, and CI/CD.

- Practical experience with LLM APIs or AI tools beyond experimental projects.

- Regular use of AI coding assistants in software development.

- Ability to make pragmatic technical trade-offs, work with evolving requirements, and communicate effectively with technical and non-technical stakeholders.

PREFERRED QUALIFICATIONS

- Experience with LLM gateways, Model Context Protocol (MCP), managed model services, or agent runtimes.

- Experience implementing AI-assisted development workflows, reusable agent skills, instructions