The Chief Technology Officer (CTO) will lead the end-to-end architecture, development, and implementation of Arken’s AI-native platform. This role requires deep expertise in AI/LLM systems combined with hands-on experience working with formal international specifications, particularly S1000D, and the ability to design AI systems that operate on strictly structured, schema-driven technical content.
The CTO will architect multi-layered agentic workflows, oversee secure and compliant AI pipelines, and lead engineering efforts involving S1000D Data Modules, Business Rules, CSDBs, and XML-driven technical documentation ecosystems, integrating them into modern AI and retrieval architectures.
Key Responsibilities
1. AI Systems Architecture
- Architect a fully modular, hexagonal AI platform supporting real-time model interchangeability and strict schema enforcement.
- Design and implement agentic workflows, multi-step reasoning systems, retrieval-augmented generation pipelines, and hybrid LLM inference architectures over structured technical standards.
- Build advanced security frameworks including prompt-injection protection, adversarial query filtering, and layered safety controls.
- Implement vector store optimization, graph-based reasoning systems, and scalable retrieval frameworks.
- Design AI systems capable of operating directly on S1000D concepts, including:
- Data Module Codes (DMC)
- Information Codes (IC)
- Applicability and effectivity
- BREX and business rules
- CSDB structures and relationships
- Build AI validation layers that respect S1000D business rules, data integrity constraints, and lifecycle states.
2. DevOps and Infrastructure (Cloud and On-Prem)
- Lead all DevOps and MLOps processes including CI/CD, container orchestration, infrastructure-as-code, and system observability.
- Deploy scalable cloud and on-prem infrastructure using Docker, Kubernetes, Terraform, and GPU orchestration.
- Support offline, air-gapped, and classified environments where S1000D content is commonly used.
- Implement enterprise-grade security architectures including zero-trust networking, audit logging, and immutable data pipelines.
3. Engineering Leadership
- Build and manage the engineering organization across AI, backend, DevOps, and security domains.
- Implement Agile processes including sprint planning, retrospectives, velocity tracking, and documentation standards.
- Establish internal training programs and enforce best practices to maintain engineering excellence.
- Oversee architectural decisions, code quality guidelines, and long-term scalability strategy.
4. Compliance and Enterprise Requirements
- Engineer solutions compliant with PHIPA, HIPAA, GDPR, SOC2, and enterprise AI governance frameworks.
- Design full auditability and traceability for AI outputs generated from regulated technical documentation.
- Ensure AI systems preserve authoritative source-of-truth behavior when operating on S1000D datasets.
- Collaborate with domain experts to align AI outputs with formal technical documentation standards.
Required Technical Expertise
The candidate must demonstrate advanced proficiency in the following areas:
AI/ML and LLM Systems
- Retrieval-augmented generation, hybrid retrieval systems, embeddings, and agent orchestration.
- LLM fine-tuning, optimization, quantization, and GPU inference.
- Security controls, adversarial robustness, and safe model deployment patterns.
S1000D & Structured Technical Standards (Mandatory)
- Hands-on experience working with the S1000D international specification in production environments.
- Strong understanding of:
- S1000D Data Modules and XML schemas
- CSDB architecture and data relationships
- BREX rules, applicability, and effectivity modeling
- Versioning, lifecycle states, and configuration control
- Experience transforming S1000D technical data into machine-readable, AI-consumable knowledge representations (graphs, indexes, embeddings, etc.).
- Ability to design AI systems that respect, enforce, and validate against S1000D rules.
Backend Engineering
- Distributed systems architecture, microservices, and domain-driven design.
- High-security API frameworks and event-driven system design.
- Scalable backend services and multi-layered platform architecture.
DevOps / MLOps
- Docker, Kubernetes, Terraform, CI/CD workflows, GPU scheduling.
- Monitoring, observability, secrets management, and infra automation.
Leadership
- Proven ability to lead multi-disciplinary engineering teams.
- Experience driving architectural strategy and technical roadmaps.
- Strong documentation and communication practices.
Minimum Qualifications
- 5+ years of engineering experience, including AI/ML specialization.
- 5+ years in senior engineering or leadership roles.
- Demonstrated ability to design and deploy production-grade LLM systems.
- Demonstrated experience working with S1000D or equivalent international technical documentation standards.
- Proficiency in Python and at least one backend language (Go or Node.js).
- Experience with cloud platforms and GPU-based workloads.
- Prior exposure to regulated industry requirements (healthcare, finance, government) is an asset.
Preferred Qualifications
- Experience building agentic AI systems or multi-reasoning pipelines.
- Previous CTO or founding engineering leadership experience.
- Experience with DGX-class hardware or on-prem GPU clusters.
- Experience integrating AI with CSDBs or structured technical documentation repositories.
- Expertise in both vector store and graph-based retrieval systems.
- Prior work with enterprise AI governance or compliance frameworks.
