Lead AI Architect

Accurate Background

1d•$191,000 - $255,000•Remote

About The Position

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.

Requirements

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

Nice To Haves

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

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