Required Field of Study:
- Bachelor’s Degree in Computer Science, Data Science, Statistics, Engineering, or a related field, or equivalent professional experience.
Minimum Years of Experience Required:
- 8+ years of professional software, data, or application development experience, including 2+ years of hands-on experience developing, implementing, or integrating AI, generative AI, LLM-powered, or agentic AI solutions in production environments, beyond proof-of-concept work.
Description of Minimum Relevant Experience Required:
- Production delivery of LLM-powered or AI solutions in cloud environments (Azure strongly preferred or equivalent), including experience with LLM integration, RAG pipeline architecture, and model evaluation beyond proof-of-concept work.
- Proficiency in Python and LLM application frameworks and SDKs; working knowledge of LLM evaluation, RAG pipeline performance tracking, and prompt versioning practices using tools such as Microsoft Foundry, Azure Promptflow, or equivalent evaluation frameworks.
- Hands-on experience with retrieval-augmented generation (RAG) pipelines, prompt engineering, embedding-based semantic retrieval, vector databases (e.g., Azure AI Search, Pinecone, or Weaviate), and LLM orchestration. Familiarity with multi-agent frameworks (e.g., LangChain, Microsoft AutoGen, or comparable) is a plus.
- Hands-on experience with knowledge graph architectures and ontology design; direct Graphwise experience is a strong plus. Working knowledge of Neo4j, Amazon Neptune, or Azure Cosmos DB provides transferable graph reasoning skills and is helpful.
- Foundational MLOps and LLMOps capability: CI/CD for ML and LLM pipelines (Azure DevOps or GitHub Actions), containerized deployment (Docker/Kubernetes), model and prompt versioning, monitoring, token cost tracking, and drift detection. Familiarity with data and workflow pipeline orchestration tools such as Azure Data Factory or Apache Airflow is a plus.
- Demonstrated ability to oversee AI vendor work through technical architecture review, acceptance criteria definition, and delivery validation.
- Strong stakeholder communication skills: proven ability to translate AI architecture decisions, risks, and tradeoffs for non-technical audiences including product and engineering stakeholders.
- Experience working in agile delivery environments with cross-functional product, engineering, data, and security teams.
Preferred Field of Study:
- Masters Degree in Computer Science, Machine Learning, Data Science, or Artificial Intelligence
Preferred Years of Experience:
- 10+ years of professional software or data engineering experience, including 3+ years of hands-on experience developing, implementing, or integrating AI, generative AI, LLM-powered, or agentic AI solutions, ideally including early-stage or greenfield AI systems delivered at enterprise scale.
Description of Preferred Relevant Experience:
- Experience contributing to or helping shape an AI or data practice — establishing engineering practices and delivery patterns. Experience helping build or scale an engineering team is beneficial.
- Domain literacy in IT governance, cybersecurity risk management, or professional certification ecosystems — sufficient to collaborate effectively with ISACA's internal subject-matter experts and align AI outputs to organizational standards.
- Experience with knowledge graph reasoning, or GraphRAG patterns relevant to professional knowledge and competency ontology use cases.
- Familiarity with bias and fairness evaluation for AI-driven recommendation systems, including output-level testing for demographic or professional skew
- Prior experience working in a non-profit, professional association, standards body, or other mission-driven organization is a plus.
- Experience collaborating across hybrid delivery models including in-house staff, contractors, and external development vendors.
- Proficiency with Azure AI platforms, or equivalent services on AWS or GCP. Azure is the primary cloud platform for this role.
- Familiarity with enterprise platforms commonly used in ISACA's ecosystem, such as Salesforce, Sitecore, and Microsoft Azure environments, is a plus.
- Working familiarity with responsible AI principles and general awareness of relevant AI regulatory trends.
Certification and Licensing Preferred:
- Microsoft Certified: Azure AI App and Agent Developer Associate (AI-103) or MLOps Engineer Associate (AI-300); prior Azure AI Engineer Associate (AI-102) or Azure Data Scientist Associate (DP-100) also recognized
- AWS Certified Generative AI Developer – Professional or Machine Learning Engineer – Associate, or Google Cloud Professional Machine Learning Engineer
Competencies/Skills Required:
- Strong customer and member focus, with the ability to design and deliver AI systems that reflect ISACA’s commitment to professional excellence and digital trust
- A senior hands-on engineer who writes production code, reviews vendor work, and helps grow an internal implementation team.
- Intellectual adaptability and continuous learning orientation; ability to rapidly develop fluency in new architectures and tools as they emerge.
- Strong problem-solving and analytical thinking; ability to evaluate tradeoffs between innovation, delivery constraints, cost, and risk in early-stage AI programs
- Excellent oral and written communication skills, with the ability to explain AI architecture decisions, technical risks, and engineering tradeoffs to technical and product stakeholders
- Collaborative working style, with the ability to build alignment across engineering, product, data, and security teams without direct authority.
- Self-starter capable of operating autonomously in environments with evolving requirements and incomplete information
- Commitment to ISACA’s mission, purpose, and values; cultural sensitivity and awareness appropriate for a global professional membership organization.
This position may require periodic onsite attendance, either currently or in the future, based on business needs. Occasional travel may also be required to attend company-sponsored events, meetings, all-hands gatherings, training sessions, or other activities necessary to support the essential functions of the role.