Job Title

The Chief Technology Officer (CTO) will lead the end-to-end architecture, development, and implementation of Arkens AI-native platform. This role requires deep expertise in AI/LLM systems combined with handson experience working with formal international specifications, particularly S1000D, and the ability to design AI systems that operate on strictly structured, schemadriven technical content. The CTO will architect multilayered agentic workflows, oversee secure and compliant AI pipelines, and lead engineering efforts involving S1000D Data Modules, Business Rules, CSDBs, and XMLdriven 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 realtime model interchangeability and strict schema enforcement. Design and implement agentic workflows, multistep reasoning systems, retrievalaugmented generation pipelines, and hybrid LLM inference architectures over structured technical standards. Build advanced security frameworks including promptinjection protection, adversarial query filtering, and layered safety controls. Implement vector store optimization, graphbased 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 OnPrem) Lead all DevOps and MLOps processes including CI/CD, container orchestration, infrastructureascode, and system observability. Deploy scalable cloud and onprem infrastructure using Docker, Kubernetes, Terraform, and GPU orchestration. Support offline, airgapped, and classified environments where S1000D content is commonly used. Implement enterprisegrade security architectures including zerotrust 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 longterm 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 sourceoftruth 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 Retrievalaugmented generation, hybrid retrieval systems, embeddings, and agent orchestration. LLM finetuning, optimization, quantization, and GPU inference. Security controls, adversarial robustness, and safe model deployment patterns. S1000D & Structured Technical Standards (Mandatory) Handson 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 machinereadable, AIconsumable 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 domaindriven design. Highsecurity API frameworks and eventdriven system design. Scalable backend services and multilayered platform architecture. DevOps / MLOps Docker, Kubernetes, Terraform, CI/CD workflows, GPU scheduling. Monitoring, observability, secrets management, and infra automation. Leadership Proven ability to lead multidisciplinary 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 productiongrade 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 GPUbased workloads. Prior exposure to regulated industry requirements (healthcare, finance, government) is an asset. Preferred Qualifications Experience building agentic AI systems or multireasoning pipelines. Previous CTO or founding engineering leadership experience. Experience with DGXclass hardware or onprem GPU clusters. Experience integrating AI with CSDBs or structured technical documentation repositories. Expertise in both vector store and graphbased retrieval systems. Prior work with enterprise AI governance or compliance frameworks. #J-18808-Ljbffr