This AI Engineer II is a mid-level engineering role responsible for designing, developing, testing, and maintaining AI platform components that enable MeridianLink's products to deliver AI-powered features consistently and reliably. You will work within the AI platform team to implement services such as model integration layers, retrieval-augmented generation infrastructure, prompt management systems, and evaluation pipelines. This role collaborates closely with Senior and Staff engineers on architecture, works directly with product engineering teams to understand their AI integration needs, and helps establish the standards and patterns teams follow when building on top of the AI platform.

WHAT YOU'LL DO

Core Platform Development

• Implement AI platform components - including model serving layers, retrieval infrastructure, prompt management, and output evaluation services - under the guidance of Senior and Staff engineers

• Develop and maintain APIs that product teams use to integrate AI capabilities into their applications

• Build observability and monitoring into platform services to track output quality, latency, cost, and system health

• Contribute to design discussions and provide input on trade-offs between simplicity, reliability, and scalability

Integration & Collaboration

• Work with product engineering teams to understand how they want to use AI capabilities and translate requirements into platform features

• Participate in technical design sessions and code reviews with product teams building on the AI platform

• Help identify patterns where multiple product teams are solving the same AI integration problem and consolidate that work

Standards, Documentation & Knowledge Sharing

• Contribute to documentation, reference implementations, and runbooks that help product teams adopt AI platform services

• Participate in defining and enforcing integration standards - API contracts, error handling patterns, cost attribution

• Mentor junior engineers (Engineer I) and help them grow their understanding of AI systems and platform design

AI Reliability & Safety

• Implement evaluation and monitoring pipelines that give teams visibility into AI output quality and model behavior

• Contribute to content safety standards and compliance guardrails appropriate for a regulated financial services environment

• Help design systems that properly handle PII, maintain audit trails, and meet data residency requirements in AI pipelines

REQUIRED QUALIFICATIONS

• 3-5 years of professional software engineering experience

• Strong proficiency in backend engineering using Python, C#/.NET, Java, or Node.js

• Solid understanding of algorithms, data structures, and system design principles

• Hands-on experience integrating large language models or ML models into production applications

• Working knowledge of retrieval-augmented generation (RAG) concepts, vector databases, and embedding pipelines

• Experience building or contributing to shared services or platform components

• Familiarity with cloud-managed AI services (AWS Bedrock, Azure OpenAI, or equivalent)

• Experience with APIs, asynchronous processing, and event-driven architectures

• Bachelor's degree in Computer Science, Software Engineering, or equivalent professional experience

PREFERRED QUALIFICATIONS

• Experience with LLM frameworks (LangChain, LlamaIndex, etc.) or prompt engineering

• Familiarity with vector databases (Pinecone, Weaviate, Milvus, or similar)

• Knowledge of containerization and orchestration (Docker, Kubernetes)

• Experience with observability tools and designing monitoring for ML/AI systems

• Prior work in regulated industries with data handling, compliance, or audit requirements

• Experience with fintech, banking, or other financial services environments

• Active daily use of AI-assisted development tools and understanding of their benefits and limitations

TECHNICAL SKILLS

• In-depth knowledge of one or more programming languages (Python, C#, Java, Node.js, etc.)

• Strong understanding of RESTful API design and web service architecture

• Proficiency with SQL and relational databases

• Experience with version control (Git) and CI/CD pipelines

• Ability to write well-tested, maintainable code and participate effectively in code reviews

YOUR GROWTH PATH

As an AI Platform Engineer II, you'll deepen your expertise in AI systems and platform design. You'll take on increasingly complex components within the AI platform, participate more actively in architectural decisions with guidance from Senior engineers, and develop a strong understanding of how AI capabilities are reliably integrated across products. Your path toward Senior Engineer (L3) involves demonstrating the ability to lead the design and implementation of significant platform components, mentor engineers effectively, and think strategically about system-wide tradeoffs and long-term maintainability.