Job Description
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.
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