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About This Role
Location
US Remote
Employment Type
Full time
Location Type
Remote
Department
Research & Development
Compensation
- $150K – $200K
MeridianLink runs a comprehensive background check, credit check, and drug test as part of our offer process.
It is not typical for offers to be made at or near the top of the salary range. The actual salary will be determined based on experience and other job-related factors permitted by law including geographical location.
Meridianlink offers:
- Insurance coverage (medical, dental, vision, life, and disability)
- Flexible paid time off
- Paid holidays
- 401(k) plan with company match
- Remote work
All compensation and benefits are subject to the terms and conditions of the underlying plans or programs, as applicable and as may be amended, terminated, or superseded from time to time.
#LI-REMOTE
About the Role
The 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.
Compensation Range: $150K - $200K Apply for this Job
Salary Context
This $150K-$200K range is above the median for AI Architect roles in our dataset (median: $175K across 37 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 9,079 AI roles we're tracking, AI Architect positions make up 1% of the market. At MeridianLink, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Architect roles pay a median of $224,070 based on 236 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($175K) sits 22% below the category median. Disclosed range: $150K to $200K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
MeridianLink AI Hiring
MeridianLink has 3 open AI roles right now. They're hiring across AI Architect, AI/ML Engineer, AI Software Engineer. Based in Remote, US. Compensation range: $177K - $200K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into AI Architect roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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