AI Architect – Internal Business Applications

MeridianLink

. Define and maintain the architectural vision, principles, standards, and governance framework for AI-first capabilities .

Posted 10/7/2026full-timeRemote • United StatesSeniorLead💰 $150,000 - $200,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in designing and implementing scalable AI architectures, focusing on observability, reliability, and compliance. Proficient in translating business requirements into AI solutions while ensuring data governance and security.

Highest-signal resume keywords

AI Architecture DesignMLOps and LLMOps ExpertisePython and SQL ProficiencyCloud Platform ExperienceEnterprise Security and Compliance

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI Systems DesignData ArchitectureAPI DesignModel ServingIntelligent Automation SolutionsObservability and MonitoringData GovernanceEvent-Driven ArchitecturesLarge Language ModelsAgent Orchestration Frameworks

Soft Skills

Executive CommunicationStakeholder ManagementChange Management

Tools & Technologies

AWSAzureGoogle Cloud PlatformDockerKubernetesDatabricksSnowflake

Industry Keywords

FintechFinancial ServicesEnterprise SaaSERPCRMHCM

Tech Stack

Tools & technologies

AWSAzureCloudDistributed SystemsDockerERPGoogle Cloud PlatformKubernetesPythonSQL

About the role

Key responsibilities & impact

Define and maintain the architectural vision, principles, standards, and governance framework for AI-first capabilities
Develop reference architectures for AI runtimes, model serving, security controls, identity management, policy enforcement, and secure enterprise data access
Establish non-functional requirements, 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
Establish enterprise data integration strategies, governance practices, and data quality standards across ERP, CRM, HCM, financial, and operational platforms
Develop APIs, reusable services, and foundational platform capabilities for scalable AI adoption
Partner with business and technology stakeholders to identify AI opportunities, evaluate 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 supporting stakeholder alignment, trust, and effective use of AI-enabled solutions

Requirements

What you’ll need

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
Experience with AWS, Azure, or Google Cloud Platform
Experience with Docker and Kubernetes
Experience with enterprise integrations, API design, and distributed systems architecture
Experience with MLOps and LLMOps tools, frameworks, and production best practices
Experience with Databricks and Snowflake
Strong executive communication and stakeholder management skills
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: experience in fintech, financial services, or enterprise SaaS environments
Preferred: hands-on experience with LLM fine-tuning, prompt engineering, RAG systems, and vector databases
Preferred: prior experience enabling or architecting AI/ML workloads in enterprise environments
Preferred: familiarity with ERP, CRM, HCM, financial systems, and integration patterns
Preferred: experience with event-driven architectures, real-time data processing, or workflow orchestration platforms
Preferred: understanding of AI governance, model monitoring, drift detection, and responsible AI frameworks
Preferred: background in computer science, engineering, or a related field
Preferred: prior experience in high-scale, product-based, or global environments

Benefits

Comp & perks

Insurance coverage (medical, dental, vision, life, and disability)
Flexible paid time off
Paid holidays
401(k) plan with company match
Remote work