Job Description:

Accurate Background is a fast-growing organization focused on providing employment background screening solutions and building trusted relationships with our clients. Accurate Background continues to exceed expectations by offering innovative background check and credentialing products.

The Lead AI Architect will play a key role in establishing and scaling Accurate Background's emerging AI capabilities as part of a new team focused on building reusable AI platforms, AI engineering standards, experimentation practices, and production-ready AI solutions.

This role will help define and operationalize an enterprise-grade AI Center of Excellence, including AI governance practices, AI lab experimentation, reusable agent and tool patterns, scalable AI solution architectures, and architecture standards for responsible AI adoption. The Lead AI Architect will help transform the traditional Software Development Lifecycle into an AI Development Lifecycle - AI-DLC, materially changing how AI-enabled solutions are designed, evaluated, deployed, governed, monitored, and continuously improved.

This role focuses on shaping AI architecture strategy and solution patterns that drive operational efficiency, improve user experiences, reduce risk, and generate measurable business and revenue value. We offer a fun, fast-paced environment with significant opportunities for growth.

Responsibilities:

• Lead the architecture, design, and technical direction for AI-enabled solutions, including AI agents, tools, GenAI-powered applications, and reusable AI services
• Define enterprise AI architecture standards, reusable patterns, reference architectures, and engineering guardrails
• Establish foundational AI Center of Excellence standards to support scalable, secure, responsible, and production-ready AI adoption
• Architect and guide development of an AI lab for experimentation, prototyping, evaluation, model testing, and rapid iteration
• Define and operationalize AI-DLC practices, including experimentation, evaluation, governance, deployment, monitoring, and continuous improvement
• Design reusable and scalable AI platforms, services, APIs, orchestration patterns, and integration layers
• Partner with business and technology leaders to identify, prioritize, and deliver AI use cases that drive operational efficiency and measurable business value
• Define architecture patterns for GenAI capabilities such as RAG, prompt engineering, agent orchestration, tool calling, memory management, and human-in-the-loop workflows
• Architect AI agent ecosystems, tool integrations, and orchestration workflows across enterprise platforms and systems
• Provide technical direction on LLMs, SLMs, embeddings, vector search, model APIs, model selection, and AI service integration
• Create architecture blueprints, technical designs, reusable frameworks, decision records, and implementation standards
• Ensure AI solutions are secure, observable, maintainable, compliant, and aligned to enterprise architecture principles
• Establish and enforce AI governance practices, including responsible AI, data protection, privacy, compliance, auditability, and risk controls
• Guide deployment, monitoring, troubleshooting, and operational readiness of AI applications and platforms
• Define evaluation frameworks, quality benchmarks, feedback loops, and AI performance measurement approaches
• Partner with stakeholders to move AI ideas from concept through architecture, experimentation, production, and scale
• Mentor engineers, architects, and product teams on AI-native architecture, AI engineering practices, and responsible AI solution design
• Influence technology selection and platform strategy across AWS, Azure, AI-native PaaS services, agent frameworks, observability tools, and enterprise integration patterns

Required Qualifications:

• Bachelor's degree in computer science or equivalent experience
• 10+ years of software engineering, solution architecture, enterprise architecture, cloud architecture, and/or AI architecture experience
• 8+ years of experience designing and delivering cloud-native solutions using modern architecture patterns
• Strong programming background, preferably with Python, and ability to guide architecture for Python-based AI services and applications
• Hands-on experience architecting or building AI-enabled applications, GenAI solutions, AI agents, or AI platforms
• Experience with AWS and/or Azure, including AI-native PaaS cloud services such as AWS Bedrock, AWS AgentCore, Azure AI Foundry, and Azure OpenAI
• Strong understanding of GenAI architecture patterns, including:
  
  • LLM/SLM model selection
  • Retrieval-Augmented Generation - RAG
  • Prompt engineering and evaluation
  • Agentic workflows
  • Tool/function calling
  • Embeddings and vector search
  • Human-in-the-loop systems
  • AI observability and monitoring
  • Model evaluation and quality benchmarking
• Experience with agent frameworks and orchestration tools such as LangGraph, Semantic Kernel, or similar technologies
• Experience with LLM ecosystems and providers such as OpenAI, Anthropic, Llama, Mistral, or similar model providers
• Experience with vector databases and retrieval systems such as Pinecone, Azure AI Search, or equivalent technologies
• Experience with tool, agent, and UI interoperability patterns, including AG-UI, A2A, MCP, registries, and reusable service layers
• Experience integrating APIs, microservices, event-driven systems, and enterprise platforms into AI workflows
• Strong understanding of modern architecture patterns including microservices, APIs, event-driven systems, cloud services, serverless patterns, and platform-based architectures
• Experience working in Agile/Scrum environments and partnering with product, engineering, operations, security, and compliance teams
• Strong understanding of AI governance, responsible AI, privacy, compliance, and risk management considerations
• Ability to translate business problems into scalable AI architecture, reusable solution patterns, and implementation roadmaps
• Strong analytical, communication, decision-making, and problem-solving skills

• Self-starter with the ability to lead through ambiguity, influence technical direction, and collaborate across teams

Preferred Qualifications:

• Experience building, leading, or contributing to an AI Center of Excellence, AI platform team, AI innovation team, or enterprise AI enablement function
• Experience defining or implementing an AI Development Lifecycle - AI-DLC
• Experience designing structured Context Engineering practices to improve model reliability, consistency, traceability, and performance
• Experience with observability and evaluation tools such as OpenTelemetry, Datadog, CloudWatch, LangSmith, or similar platforms
• CI/CD, containerization, infrastructure automation, and cloud deployment patterns
• Architecting AI solutions in regulated, compliance-driven, or risk-sensitive environments
• Experience delivering AI solutions for operational automation, customer experience, internal productivity, or revenue-generating products.
• Familiarity with AI governance frameworks such as ISO/IEC 42001, NIST AI RMF, or similar standards
• Experience defining reusable architecture patterns for multi-agent orchestration, deterministic workflows, human review, policy enforcement, auditability, and production monitoring
• Experience creating executive-level architecture artifacts, technology roadmaps, solution blueprints, and governance models

AI Mindset:

• Balance rapid experimentation with disciplined architecture, governance, and engineering practices
• Design AI platforms and solutions for scalability, reusability, observability, security, and compliance from the start
• Focus on measurable business outcomes, operational impact, risk reduction, and value creation
• Embrace ambiguity while helping define standards, patterns, and guardrails for emerging AI capabilities
• Build systems with continuous evaluation, monitoring, feedback, and improvement
• Apply responsible AI principles across architecture, design, deployment, and operations
• Promote reusable AI patterns that accelerate delivery while reducing duplication, inconsistency, and operational risk
• Think in terms of platforms, ecosystems, agent interoperability, reusable tools, and enterprise-scale adoption

Working Conditions:

• The company environment is dynamic and reflective of rapid growth
• Friendly, helpful, open, and all inclusive
• Fast paced with frequent changes
• Sits and works at desk computer-keyboard for extended periods of time
• Works with others

The annual base salary for this position ranges from $191,000 - $255,000. Pay will vary depending on job-related knowledge, skills, experience, and relevant education and training. This position may also be eligible for an annual performance-based bonus, commission, or other variable pay plan. The Company also offers a full range of benefits, including medical, dental, and 401k. Your recruiter can share more details about the specific compensation package during the hiring process.