Lead the technical development strategy for Prism. Translate business and product requirements into scalable, secure AI platform designs. Establish patterns for AI pipelines, agent orchestration, and services that internal systems engineers and AI consumers can build on confidently - coordinating with adjacent infrastructure teams where the platform depends on their systems.
Continuously identify what's next. Stay ahead of the AI landscape. Proactively evaluate emerging models, frameworks, and tooling - and translate that into a prioritized roadmap of improvements that deliver real benefits: cost savings, efficiency gains, new capabilities.
Own AI efficiency and cost management for the Prism platform. Drive measurable improvements across inference cost, token usage, caching strategies, model selection, and prompt orchestration.
Govern AI responsibly. Implement AI governance frameworks and data stewardship practices aligned with SOX, SOC 2, PCI, PII, and data residency requirements. Partner with Security, Compliance, Legal, and Risk to ensure AI systems are trustworthy by design — not audited after the fact.
- Work alongside AI support teams to raise technical capability across the group. Lead technical reviews, provide technical guidance, and help the team grow — without losing your own hands-on output.
- Requirements:
- 10+ years of enterprise software development experience
Experience working with agentic/AI software development workflows
Strong proficiency in JavaScript and TypeScript, including modern ES6+ syntax and static typing patterns
Hands-on experience building and running server-side applications with Node.js (e.g., REST APIs, async I/O, package management via npm/yarn)
Hands-on experience with LLM APIs, prompt engineering, custom skill development, and multi-turn orchestration
Production experience building and maintaining AI standard protocols - server design, tool registration, and context management
Experience with orchestration platforms
Strong MongoDB expertise, schema design, vector/embedding storage, and aggregation pipelines for AI knowledge retrieval
Working knowledge of enterprise compliance requirements: SOX, SOC 2 Type II, PCI-DSS, PII handling, and data residency
Hands-on experience applying AI to business use cases, beyond purely technical or engineering applications- Demonstrated ability to drive AI adoption through training, documentation, and enablement programs
A track record of delivering cost and efficiency improvements through inference optimization, caching, or model routing
Strong communication skills, able to present technical architecture to non-technical stakeholders and write clearly for both audiences