Note: The job is a remote job and is open to candidates in USA. Mindlance is recruiting for a Director, AI Security Platform Engineer to lead the development of a multi-service security compliance platform. The role combines hands-on distributed systems architecture and production engineering with team leadership, AI-agent API development, enterprise integrations, compliance data modeling, and cross-organizational technical coordination.
Responsibilities
- Define the end-to-end system architecture for a multi-service security compliance platform (evidence collection, controls system-of-record, risk scoring) — then build its core services. Own technology selection, protocol decisions, and data model design with full accountability, and implement them in production code
- Lead, grow, and mentor a small, nimble engineering team — setting technical direction, driving code and design review, unblocking engineers, and owning hiring, delivery planning, and performance development. Lead as a player-coach who ships alongside the team and keeps a lean team high-leverage rather than adding process overhead
- Design and build the agent consumption layer — how AI agents across the security organization Client, query, and reason about compliance state — including authentication models, rate limiting, and consumption patterns optimized for agent workloads
- Design and implement how security controls decompose from policy to implementation, how evidence maps to verdicts, and how the audit trail remains immutable, versioned, and OSCAL-aligned. Build the schemas, migrations, and query interfaces
- Build integration services connecting enterprise architecture platforms, ITSM systems, cloud security tooling, and security data lakes into a coherent application identity and compliance graph. Resolve competing data sources through reconciliation logic you write and operate
- Own architectural decision records and technology selection — protocol choices, storage engines, data formats, deployment topology — documenting rationale for the team and consumers
- Design and implement the contribution model so other security engineering teams can extend the platform with new evidence adapters, control domains, and consumer integrations without architectural drift
- Coordinate across enterprise architecture, security governance, security operations, and compliance teams to define integration contracts, data flow agreements, and adoption roadmaps — influencing through working code and design quality, not just presentations
- Design and implement multi-tenant consumption patterns with high concurrency and low-latency query paths for AI agent consumers, ensuring the platform supports production-scale adoption
Skills
- * Master's in Computer Science, Cybersecurity, or related STEM field, or commensurate experience in role
- * 8+ years in software engineering or architecture with production distributed systems at enterprise scale. 5+ years in security engineering, compliance automation, or platform development
- * Demonstrated experience designing and shipping platforms consumed by other engineering teams and multiple customer personas — you build for builders
- * Track record of leading a small engineering team as a hands-on player-coach — hiring, mentoring, and growing engineers while remaining a top individual contributor
- * Track record of cross-organizational technical leadership — influencing architecture decisions across team boundaries through design quality and stakeholder alignment, including without direct authority
- * Experience with regulatory compliance frameworks (NIST, ISO 27001, EU AI Act) in technical implementation, not just assessment
- * Strong communication skills with ability to brief senior leadership, draft architectural decision records, and coordinate across security, engineering, privacy, and governance teams
- * Experience defining and operating a NIST OSCAL-native compliance system-of-record across multiple OSCAL models (Catalog, Profile, Component Definition, SSP, Assessment Results, POA&M)
- * Track record building agent-native API surfaces (MCP or equivalent) consumed by AI systems, not just human clients
- * Prior success establishing an inner-source platform with an active contributor community across multiple engineering teams
Company Overview
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