Sr AI Architect - Internal Business Applications

MeridianLink

34d•$150,000 - $200,000

About The Position

The Sr. AI Architect, Internal Business Applications, defines and leads the reference architecture for AI and intelligent automation across MeridianLink’s internal business operations. The role enables secure, scalable AI-powered workflows and enterprise-wide business transformation, designing, scoping, and implementing complex AI systems across business process optimization, AI workflow design, data architecture, enterprise integration, and organizational change. The position owns the end-to-end design and development of AI solutions and intelligent automation platforms that support process optimization, advanced analytics, decision intelligence, and autonomous workflows across Finance, Human Resources, Operations, Customer Success, and other business functions. It designs secure, reliable, and scalable solutions aligned with operational requirements, regulatory standards, and enterprise governance frameworks, combining technical expertise with hands-on architecture, engineering leadership, and business enablement.

Requirements

At least 8 years of experience in software architecture, platform engineering, AI/ML systems design, data architecture, or related technical disciplines.
At least 4 years of experience designing and deploying production-scale AI systems, including large language models, agent orchestration frameworks, and intelligent automation solutions.
Deep expertise in modern AI architectures, including LLM serving, Retrieval-Augmented Generation (RAG), agentic orchestration, and AI workflow design.
Demonstrated success designing and implementing enterprise-scale systems with a focus on observability, reliability, scalability, and cost optimization.
Hands-on expertise with Python and SQL.
Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform.
Experience with containerization and orchestration technologies, including Docker and Kubernetes.
Expertise in enterprise integrations, API design, and distributed systems architecture.
Expertise in MLOps and LLMOps tools, frameworks, and production best practices.
Experience with modern data platforms such as Databricks and Snowflake.
Strong executive communication and stakeholder management skills, including the ability to influence decisions and explain complex technical concepts to technical and non-technical audiences.
Ability to provide architectural leadership from strategy and design through implementation and operationalization.
Strong understanding of enterprise security, identity and access management, data governance, privacy requirements, and compliance frameworks.

Nice To Haves

Experience in fintech, financial services, or enterprise SaaS environments.
Hands-on experience with LLM fine-tuning, prompt engineering, RAG systems, and vector databases.
Prior experience enabling or architecting AI/ML workloads in enterprise environments.
Familiarity with enterprise application ecosystems, including ERP, CRM, HCM, and financial systems, and their integration patterns.
Experience with event-driven architectures, real-time data processing, or workflow orchestration platforms.
Understanding of AI governance, model monitoring, drift detection, and responsible AI frameworks.
Background in computer science, engineering, or a related field.
Prior experience in high-scale, product-based, or global environments.

Responsibilities

Define and maintain the architectural vision, principles, standards, and governance framework for AI-first capabilities, including intelligent automation, LLM orchestration, agentic workflows, and enterprise integrations.
Develop reference architectures for AI runtimes, model serving, security controls, identity management, policy enforcement, and secure enterprise data access.
Establish non-functional requirements for reliability, performance, scalability, cost optimization, and compliance, and define service-level objectives and validation methodologies.
Translate business and operational requirements from Finance, Human Resources, Operations, Customer Success, and other functions into scalable AI solution architectures and implementation strategies.
Design and implement secure, compliant intelligent automation and decision-support solutions, including document processing, data extraction, workflow automation, and business process optimization.
Establish enterprise data integration strategies, governance practices, and data quality standards to support AI readiness across ERP, CRM, HCM, financial, and operational platforms.
Develop APIs, reusable services, and foundational platform capabilities that enable scalable AI adoption across business teams and technology organizations.
Partner with business and technology stakeholders to identify high-impact AI opportunities, evaluate solution feasibility, and define implementation roadmaps.
Contribute to MLOps and LLMOps capabilities, including pipeline standardization, model observability, monitoring, lifecycle management, and production governance.
Lead adoption and change management efforts to support stakeholder alignment, trust, and effective use of AI-enabled solutions and intelligent automation.