Enterprise AI Architect
Our client is aestablished investment management firm with a long-standing presence in the asset management industry. The firm manages investment products for a mix of institutional and individual investors and is known for taking a research-driven, long-term approach to investing.
The business has a collaborative culture with strong connectivity across investment, client-facing, operations, technology, marketing, and corporate functions. Employees tend to work closely with senior stakeholders, and the environment offers exposure to both strategic firmwide initiatives and day-to-day investment management operations.
This is a strong platform for someone who is interested in joining a stable, well-regarded financial services organization that is continuing to invest in its infrastructure, technology, and client experience.
The AI Solutions Architect will help shape and deliver enterprise-wide AI and automation initiatives that improve business processes, decision-making, and operational efficiency. This person will work closely with business leaders, technology teams, security, data, infrastructure, and application groups to identify opportunities for AI adoption and translate them into scalable, secure solutions.
This role requires a mix of enterprise architecture experience and hands-on AI implementation skills, including experience with generative AI, LLM-based tools, cloud AI services, enterprise integrations, AI governance, automation frameworks, and secure deployment practices.
Key Responsibilities:
Design and deliver enterprise AI solutions using cloud-based AI platforms, large language models, automation tools, and modern data technologies.
Lead the buildout of generative AI solutions, including chat-based assistants, knowledge retrieval tools, AI agents, workflow automation, and business-facing copilots.
Create secure integration patterns that allow AI tools to connect with internal systems, APIs, applications, and enterprise data sources.
Partner with business teams to understand use cases, refine requirements, and build practical AI solutions that support self-service and productivity.
Develop reusable frameworks, prototypes, reference architectures, and proof-of-concepts to accelerate AI adoption across the organization.
Support AI governance efforts, including responsible AI practices, security standards, model oversight, prompt management, compliance, monitoring, and lifecycle controls.
Provide technical guidance around AI orchestration, prompt design, embeddings, vector search, data access, and enterprise application integration.
Work with cybersecurity, infrastructure, data, and development teams to ensure AI platforms are reliable, secure, and ready for enterprise use.
Evaluate emerging AI tools, integration standards, governance approaches, and automation platforms to help inform the broader AI roadmap.
Use AI-enabled development tools to improve engineering productivity, solution design, and delivery quality.