Detailed Job Description:
Position Title: Director, AI Security Platform Engineer Location: Remote Duration: 12 Months CTH/TTH
About the Role:
The Director, AI Security Platform Engineer will serve as a player-coach engineering leader responsible for defining, building, and evolving a multi-service security compliance platform that serves as the connective tissue across the AI security program while leading and growing the small, nimble team that builds it. As AI security capabilities mature from individual tools into an interconnected program spanning agent governance, edge defense, continuous testing, compliance evidence, and security operations the organization needs a unifying technical architecture that connects evidence collection, control management, risk scoring, and compliance reporting into a coherent platform.
This is a hands-on leadership role: you will manage a small engineering team while remaining the platform's strongest individual contributor writing production code, owning the architecture, and coordinating integration contracts across teams. It is a player-coach role weighted toward personal delivery, approximately 60% hands-on engineering and architecture, 40% people leadership and cross-organizational coordination. You lead by building alongside the team, not from above it: setting technical direction through the code you ship, growing engineers through direct mentorship and review, and keeping a small team punching above its weight.
The ideal candidate will combine senior-level distributed systems engineering with the ability to lead a lean team and influence across organizational boundaries someone who can resolve a protocol choice with an ADR and implement the service that proves it out, unblock and mentor an engineer on the team, and align three peer teams on the integration contract, all in the same day.
Responsibilities:
- Platform Architecture and Core Development: 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.
- Team Leadership and Mentorship: 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.
- Agent-Native API Engineering: 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.
- Compliance Data Model Development: 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.
- Enterprise Integration Engineering: 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.
- Technical Decision Ownership: Own architectural decision records and technology selection protocol choices, storage engines, data formats, deployment topology documenting rationale for the team and consumers.
- Inner-Source Platform Engineering: 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.
- Cross-Organizational Technical Leadership: 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.
- Scale Engineering: 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.
Technical Skills :
- Distributed systems architecture at enterprise scale: high-availability patterns, service mesh, event-driven architectures, and multi-service coordination.
- Proficiency in Python and at least one additional systems language (Go, TypeScript, or Rust) for production platform development.
- API design for platform consumers: REST, GraphQL, streaming/SSE, and protocol selection trade-offs.
- Security architecture: mTLS, OAuth2 client credentials, service identity, zero-trust networking, and API security patterns.
- Data modeling for compliance and audit domains: immutable stores, versioned records, lineage tracking, and temporal queries.
- Kubernetes and cloud infrastructure architecture (AWS preferred) including service mesh, secrets management, and multi-environment deployment.
- Experience with AI/ML agent frameworks and agentic system design: multi-agent orchestration, tool libraries, Model Context Protocol (MCP) or similar agent-native API patterns.
- NIST OSCAL expertise across multiple models (Catalog, Profile, Component Definition, SSP, Assessment Plan, Assessment Results, POA&M) or equivalent structured compliance standards.
- Data lake and analytics architecture: object storage, columnar formats (Parquet/Iceberg), query engines, and telemetry pipelines.
- Graph-based modeling: labeled property graphs, ontology design, and lineage traversal for control hierarchies and compliance relationships.
- Enterprise architecture integration patterns: application portfolio management platforms, CMDB reconciliation, and cross-system identity resolution.
- Inner-source or open-source program design: contribution models, developer experience, API documentation, and platform onboarding.
- Architectural decision documentation practices (ADRs) and cross-team technical communication.
Required 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.
Preferred Skills:
- 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.
Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans.
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