Chief Technology Officer (CTO) AI/ML & Agentic Systems & S1000D Arch

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 Responsibilities1. 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.