AI Solutions Architect

Senior architect with recent, hands‑on experience orchestrating modern intranet platforms together with AI agents and services. We are seeking an AI Solution Architect, Vice President to serve as a senior technology leader responsible for shaping and driving the AI strategy for our Internal Audit function. In this role, you will provide architectural leadership to design, build, and operate production‑grade AI systems that deliver conversational, predictive, and generative capabilities at enterprise scale This is a hands‑on architectural leadership role requiring deep expertise in Generative AI, large language models (LLMs), retrieval‑augmented generation (RAG), AI orchestration frameworks, and enterprise integration patterns. You will partner closely with Internal Audit SMEs, data engineering, security, and infrastructure teams to ensure AI solutions are explainable, secure, compliant, and operationally resilient, while materially improving audit quality, productivity, and insight generation. What You Will Be Responsible For AI Architecture & Platform Strategy Lead the end‑to‑end architecture for AI‑enabled platforms supporting Internal Audit, integrating LLMs, machine learning models, enterprise data platforms, and core systems Define scalable, reusable architectural patterns for conversational assistants, generative insights, predictive analytics, and continuous auditing use cases Act as the technical authority for AI architecture within Internal Audit Technology, setting standards and guiding architectural decisions across initiatives Lead the design and implementation of AI orchestration frameworks to enable scalable, multi‑step reasoning and agent‑based workflows Architect solutions using frameworks such as LangChain (or equivalent) and cloud‑native capabilities (e.g., managed AI/agent services) Retrieval‑augmented generation (RAG) across structured and unstructured data Guardrails, validation layers, and hallucination‑mitigation techniques Define and own model selection, evaluation, and benchmarking frameworks, balancing performance, cost, latency, explainability, and risk Build & Production Operations Lead delivery of production‑ready AI systems, including model deployment, APIs, orchestration pipelines, and enterprise integrations Establish and mature LLMOps / MLOps practices, including versioning, monitoring, evaluation, logging, rollback strategies, and cost controls Ensure platforms meet enterprise standards for availability, scalability, performance, resilience, and reliability Embed AI governance and model risk management into system design Implement safeguards for data privacy, security, bias detection, explainability, and auditability Partner with Internal Audit, Risk, Compliance, and Legal teams to align solutions with regulatory and internal policy requirements Translate audit and risk challenges into high‑impact AI use cases (e.g., control testing automation, issue identification, narrative generation, continuous auditing) Guide experimentation and proof‑of‑concepts while ensuring a clear path to production Stay current on emerging AI technologies and recommend pragmatic adoption strategies What We Value The skills that will help you succeed in this role include: End‑to‑end AI architecture and solution design for enterprise, production‑grade systems Hands‑on expertise with Generative AI, including LLMs, prompt engineering, embeddings, RAG, and agent‑based workflows AI orchestration and workflow design skills for multi‑step reasoning, validation, and automation Strong engineering and operational skills to deliver scalable, reliable platforms with robust LLMOps/MLOps practices Responsible AI and governance skills, including model risk management, security, privacy, explainability, and auditability Data engineering and integration skills, leveraging enterprise data platforms, APIs, and distributed systems Analytical problem‑solving skills to translate audit and risk challenges into high‑impact AI use cases Stakeholder partnership and influence skills across audit, risk, security, and technology teams Clear technical communication and mentoring skills for technical and non‑technical audiences Continuous learning mindset to stay current with evolving AI technologies and best practices Education & Preferred Qualifications Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field 10+ years of experience designing and delivering enterprise software or data platforms, with 3+ years focused on AI / ML solutions Hands‑on experience with large language models and modern AI frameworks (prompt engineering, embeddings, RAG, agents) Strong background in cloud platforms (Azure or AWS) and microservices‑based architectures Experience operationalizing AI systems with MLOps / LLMOps tooling and practices Solid understanding of data engineering, APIs, and distributed systems Proven ability to design systems with security, privacy, and regulatory considerations Nice to Have Experience supporting Internal Audit, Legal, or Risk Corporate functions Familiarity with model risk management or AI governance frameworks Experience with vector databases, semantic search, or enterprise data catalogs Exposure to process mining, control testing automation, or continuous auditing #J-18808-Ljbffr