AI Solutions Architect

Job description

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

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Worksite address

boston, MA, 02298, US

Who can apply

Review the original listing for work authorization, qualifications and employer requirements.