Job description

About the Role The Sr. AI Architect, Internal Business Applications, will define and lead the reference architecture for AI and intelligent automation across MeridianLink's internal business operations, enabling secure, scalable AI-powered workflows and accelerating enterprise-wide business transformation. This role is responsible for designing, scoping, and implementing complex AI systems that operate at the intersection of business process optimization, AI workflow design, data architecture, enterprise integration, and organizational change. The position owns the end-to-end design and development of modern AI solutions and intelligent automation platforms that drive process optimization, advanced analytics, decision intelligence, and autonomous workflows across Finance, Human Resources, Operations, Customer Success, and other business functions. Responsibilities include designing secure, reliable, and scalable solutions aligned with operational requirements, regulatory standards, and enterprise governance frameworks. The ideal candidate combines deep technical expertise with a hands-on approach to architecture, engineering leadership, and business enablement. Responsibilities - Define and maintain the architectural vision, principles, standards, and governance framework for AI-first capabilities, including intelligent automation, large language model (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, including reliability, performance, scalability, cost optimization, and compliance standards, while defining 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 that 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 utilization of AI-enabled solutions and intelligent automation capabilities. Required Qualifications - 8+ years of experience in software architecture, platform engineering, AI/ML systems design, data architecture, or related technical disciplines. - 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 strong focus on observability, reliability, scalability, and cost optimization. - Strong hands-on expertise with Python and SQL; cloud platforms such as AWS, Azure, or Google Cloud Platform; containerization and orchestration technologies, including Docker and Kubernetes; enterprise integrations, API design, and distributed systems architecture; MLOps and LLMOps tools, frameworks, and production best practices; and modern data platforms such as Databricks and Snowflake. - Strong executive communication and stakeholder management skills, with the ability to influence decisions and communicate complex technical concepts to both technical and non-technical audiences. - Proven 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. Preferred Qualifications - 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 (ERP, CRM, HCM, financial systems) and 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.

Requirements

Must-haves

Develop reference architectures for AI runtimes, model serving, security controls, identity management, policy enforcement, and secure enterprise data access.
Establish non-functional requirements, including reliability, performance, scalability, cost optimization, and compliance standards, while defining 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 that 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 utilization of AI-enabled solutions and intelligent automation capabilities.
8+ years of experience in software architecture, platform engineering, AI/ML systems design, data architecture, or related technical disciplines.
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 strong focus on observability, reliability, scalability, and cost optimization.
Strong executive communication and stakeholder management skills, with the ability to influence decisions and communicate complex technical concepts to both technical and non-technical audiences.
Proven 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 (ERP, CRM, HCM, financial systems) and 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.

Application prep

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Keywords to mirror

aillmmlpythonsqlawsazuredockerkubernetesapi

Platform: ashby
Effort: quick

Benefits

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