AI Platform Technical Owner (Contractor)

ISACA

About The Position

ISACA is a global professional association and learning organization that leverages the expertise of its 180,000+ members who work in digital trust fields such as information security, governance, assurance, risk, privacy and quality. It has a presence in 188 countries, including 225 chapters worldwide. Through the ISACA Foundation, ISACA supports IT education and career pathways for underresourced and underrepresented populations. Overview 12-month contract, ideally 1099 | Enterprise AI & Data Platform | Azure preferred ISACA is seeking a senior, hands-on AI Platform Technical Owner (contractor) to own the AI infrastructure and foundational capabilities critical to our strategy. The AI infrastructure must integrate with an ongoing complex professional publishing operation and multiple active data and AI-driven projects. This role requires a strategic and hands-on leader to drive the design, development, and delivery of AI and data solutions that can scale to support ongoing enterprise AI and data initiatives. The primary focus of this role will be working with internal stakeholders and external contractors to lead the development of a customer facing. The next product is a customer Career Journey application with an MVP release in Q4 2026. This role will establish a reusable AI and data foundation supporting current and future initiatives. The role is responsible for translating technical requirements into a prioritized platform roadmap, driving delivery of a production-grade AI knowledge platform, and documenting the organization’s AI and data architecture as it evolves across these activities. This role must balance immediate platform needs with long-term scalability, security, and operability. You will partner closely with ISACA’s engineering, data, security, content, and product teams as well as external consulting teams to define business and technical requirements for priority AI projects, reuseable AI and ML Ops processes to support AI development and monitoring initiatives, and strategic level technical roadmaps for future AI initiatives to t enable predictable, secure, and cost-effective AI outcomes across ISACA’s AI efforts. You will prioritize infrastructure work against platform requirements; define clear technical specifications and acceptance criteria; orchestrate cross-functional delivery; validate production readiness; and measure success with concrete KPIs (availability, MTTR, latency, cost, developer onboarding time, and platform adoption). You will coordinate with the PMO for dependencies across other initiatives. A key group of stakeholders are the current knowledge content group. They run a complex and disciplined publishing operation that is ongoing. They are in the middle of a transition to digital first process and have begun some AI work with topical classification. This group is responsible for our deep professional content including books, frameworks, learning material, and assessments along with blogs, papers, including print. This content prepares our customers and members for prestigious certifications. The group is in the middle of a transition to digital first publishing, and have begun some AI work with topical classification. It is likely that AI curation, semantics management, models, taxonomies and mappings will fall within this group to leverage knowledge, minimize slack, maintain high quality. This proprietary IP generates ISACA’s largest revenue stream and is foundational to other channels. There are also membership, event and community elements. We plan to increase our use of personalization across the spectrum. Success in this role means a reliable, maintainable AI foundation that accelerates product delivery, reduces operational risk, and provides the foundational infrastructure that multiple applications can build upon while remaining extensible for future AI use cases.

Requirements

Experience with complex digital knowledge content and high accuracy (high 90s) AI such as legal, regulatory, compliance, science, learning and other exacting subject domains.
Knowledge of current and emerging neuro symbolic architectures and/or federated AI.
Strong foundation in data science or applied software engineering, with multiple years of professional experience in one or more of these disciplines.
Demonstrated hands-on experience delivering AI-enabled systems into production environments, beyond proof-of-concept work.
Proven ability to operate effectively in environments with evolving requirements and incomplete information.Experience collaborating across engineering and data teams in enterprise or complex organizational contexts.
Experience designing scalable, reusable AI platform components.
Hands-on experience with large language models in real-world, production scenarios with as much depth in inference and semantics as recursion and properties.
Experience implementing embedding techniques for semantic retrieval and reasoning.
Experience with knowledge engineering including ontology, graphs/GraphRAG, enrichment (topics, NER, rich linkage, NLP, etc.), reasoning and SME opinionated curation.
Practical application of retrieval-augmented generation patterns.
Experience working with vector and graph databases and semantic search systems.
Strong understanding of model behavior, performance tradeoffs, and operational constraints in production environments.
Experience building and integrating AI-enabled APIs and backend services.
Experience with modern service frameworks and patterns (language-agnostic).
Familiarity with exposing AI capabilities via RESTful interfaces.
Understanding of API management concepts such as authentication, throttling, and secure access patterns.
Experience working with relational databases for application state and metadata.
Strong experience with vector and graph type-based data storage and retrieval.
Knowledge of content lakes, master repositories and uncoupled product repositories.
Experience with cloud search services including vector and graph search capabilities.
Hands-on experience deploying AI-enabled solutions in cloud environments.
Experience with serverless and managed compute services.
Experience with containerized application deployment and container registries.
Experience with cloud-native monitoring and observability tools.
Experience with cloud AI services for model management, governance, and evaluation.
Knowledge of scalability, reliability, and operational considerations for AI cloud workload.
Strong problem-solving and analytical thinking skills.
Ability to explain complex AI concepts in clear, practical terms.
Comfortable working in ambiguous and evolving problem spaces.
Collaborative mindset with the ability to influence without authority.
Pragmatic approach to balancing innovation with delivery realities.

Nice To Haves

Microsoft Azure – Strongly Preferred

Responsibilities

Define AI and data platform strategies aligned with business goals; lead cross-functional teams to deliver impactful solutions.
Serve as a subject matter expert to business stakeholders, guiding decision-making in ambiguous or early-stage problem spaces and advising on appropriate AI usage and tradeoffs.
Partner with clients to assess needs, design roadmaps, and guide AI adoption and data maturity.
Communicate AI concepts, risks, and tradeoffs clearly to technical and non-technical audiences and represent platform consumers when negotiating priorities.
Work closely with ISACA’s AI committee to ensure all AI development projects are governed, monitored, and reported in accordance with enterprise AI governance policies.
Support evolution of the enterprise AI platform strategy and roadmap, ensuring alignment between platform capabilities and long-term organizational AI direction.
Operate and scale a horizontal AI engineering foundation, including model lifecycle management (development, versioning, fine-tuning, CI/CD, and release processes) and production-grade ModelOps practices.
Define and implement AI observability and operational controls, including monitoring data drift, model performance, latency, throughput, and incident response with MTTR targets.
Contribute to AI FinOps practices through cost monitoring, budget guardrails, and optimization strategies to ensure scalable, cost-effective AI services.
Ensure complete integration between solutions like ISACA’s Career Journey offering and publishing platform technology, with integrated offerings tailored to complex professional content that is proprietary and the primary revenue driver for ISACA.
Provide hands-on technical leadership embedded within delivery teams, contributing to the design, implementation, and deploying AI components while guiding architectural decisions and mitigating technical risk and debt.
Define integration patterns and APIs, deployment standards, and production readiness criteria; advise on AI or data specific contractual concerns, frontend/API/data/AI integration patterns, etc.
Work in agile, iterative cycles with engineering teams: validate approaches through prototyping, testing, and incremental delivery; identify technical risks early and propose pragmatic mitigations; transition POCs to production.
Ensure that the AI platform supports the full range of publishing cycles from years to real-time, including A/B content management features like user-centric approach.
Work with the AI committee to define and embed responsible AI controls and TRiSM practices across the platform lifecycle, including policy enforcement, auditability, explainability, and proactive identification and mitigation of technical and regulatory risks.
Establish governance guardrails that prevent one-off or tightly coupled implementations, promote reusable architectural patterns, and ensure long-term platform maintainability and compliance.
Stay current on regulatory, legal, and ethical AI considerations and incorporate appropriate controls.
Recommend governance across content operations, products, and horizontal divisions including complex content set publishing coordinated with product updates and releases that include software features interoperability digital knowledge content
Collaborate with internal stakeholders to coordinate external consulting partners and advise on vendor negotiations and deliverable alignment with platform objectives.
Advise build-vs-buy decisions, vendor selection frameworks, and evaluation of COTS or cloud-native solutions, ensuring architectural flexibility and avoiding dependency on any single model provider.
Assist in platform change management: developer onboarding, training, documentation, and adoption programs to increase platform usage and reduce shadow/abandonment.
Create enablement of materials and processes that reduce time to value for product teams and improve developer productivity.
Define, track, and report platform success metrics spanning adoption, release cadence, developer productivity, reliability, and cost; use telemetry and stakeholder feedback to continuously refine platform capabilities.
Use telemetry and stakeholder feedback to iterate on platform features, observability, and governance.