API Platform Owner

Axos Bank Target Range: $150,000.00 /Yr. - $170,000.00 /Yr Actual starting pay will vary based on factors including, but not limited to, geographic location, experience, skills, specialty, and education. Eligible for an Annual Discretionary Cash Bonus Target: 10% Eligible for an Annual Discretionary Restricted Stock Units Bonus Target: 10% These discretionary target bonuses may be awarded semi-annually based upon your achievement of performance goals and targets. About This Job At Axos, we're building the next generation of banking technology. The API Platform Owner will lead the strategy, governance, and evolution of our enterprise API platform while expanding its capabilities as a secure AI gateway. This role drives the standards, tools, and frameworks that enable teams to rapidly deliver innovative digital and AI-powered solutions while maintaining enterprise-grade security, compliance, reliability, and observability. Working closely with engineering, architecture, AI/ML, and security teams, the API Platform Owner will define platform roadmaps, establish governance and lifecycle management standards, and oversee modernization efforts across APIs, AI integrations, and Model Context Protocol (MCP) services on Apigee X. This leader will play a critical role in accelerating innovation, improving developer experience, and ensuring Axos remains at the forefront of secure, scalable, AI-enabled financial technology. Responsibilities: Design and operate a comprehensive API and AI gateway governance framework on Apigee X, covering REST/SOAP services, LLM proxies, and MCP servers under a single policy plane Own governance of the platforms and of the AI solutions and workloads built on them — define and enforce solution standards, risk tiering, approval and promotion gates, observability requirements, and compliance attestation, holding consuming engineering teams accountable to them Develop enterprise-wide API and AI consumption strategy, including model routing, token-aware rate limiting, semantic caching, and cost-attribution for LLM workloads Establish MCP server governance patterns — auth (OAuth/Entra ID), audit, rate limiting, and policy enforcement on every MCP tool call exposed to internal AI agents or external consumers Implement AI safety controls for prompt-injection defense, PII detection/redaction, jailbreak detection, and response filtering Define LLM API design standards covering provider abstraction (Vertex AI, Anthropic, OpenAI, Bedrock), failover, prompt/response logging, and red-team evaluation Create security and compliance protocols for the API and AI ecosystem aligned to financial services regulatory requirements (BSA, GLBA, CCPA) and emerging AI guidance (NIST AI RMF, ISO/IEC 42001) Design scalable microservices and AI-agent integration architectures, including agent-to-tool and agent-to-API interaction patterns through the gateway Develop platform performance, reliability, and observability strategies — including token consumption telemetry, model latency tracking, and SLO management for AI endpoints Partner with the Center of Excellence (CoE) to define citizen developer and AI agent enablement patterns that route all programmatic AI access through the governed gateway Actively contribute to technical implementation across Apigee X proxies, shared flows, Model Armor templates, and MCP server integration when team resources are constrained Team Management Requirements: Performance management and professional development, including building team capability in AI and LLM operations Resource allocation and project planning across concurrent API modernization (Apigee Edge → Apigee X) and AI gateway buildout Technical coaching on Apigee X policies, MCP protocol patterns, and LLM integration best practices Ability to step in and perform hands-on engineering work on Apigee proxies, shared flows, MCP integrations, and AI policy configuration Requirements: 5+ years enterprise API architecture experience, with at least 1+ years on Apigee X or another AI-capable gateway Apigee platform knowledge — proxies, shared flows, policy chains, environments, and integration with Google Cloud services Demonstrated experience designing, deploying, or governing LLM API integrations (OpenAI, Anthropic, Vertex AI, AWS Bedrock, or equivalent) in a production environment Working knowledge of the Model Context Protocol (MCP) — server implementation patterns, tool/resource exposure, transport options (stdio, HTTP/SSE), and authentication models Familiarity with AI-specific security risks — prompt injection, data exfiltration through tool calls, indirect injection, jailbreaks — and the gateway-layer controls that mitigate them Proven track record of cross-functional delivery Experience in Python, Java, or Node.js, with the ability to read and modify Apigee policy XML/JavaScript and to prototype LLM client and MCP server code Experience in microservices and cloud architecture, particularly on Google Cloud (Apigee X, Vertex AI, IAM, VPC) Advanced understanding of OAuth, OIDC, mTLS, JWT, and API security protocols, including how these apply to AI agent authentication and delegated access Experience with CI/CD pipeline design for API proxies and policy artifacts, with promotion gates suitable for AI-impacting changes Preferred Certifications: Apigee Certified Professional Google Cloud Professional Cloud Architect or Professional Machine Learning Engineer Cloud Architecture certification (AWS / Azure / GCP) TOGAF or similar enterprise architecture certification AI governance / responsible AI credential (e.g., IAPP AIGP) — desirable Preferred Skills: Financial services technology background with familiarity in BSA, GLBA, CCPA, and model risk management (SR 11-7) considerations as they extend to GenAI Hands-on experience with Apigee X AI/ML proxy patterns, Model Armor configuration, and Vertex AI integration Experience building or deploying MCP servers in p