Lead AI Architect

Job Description

Accurate Background is a fast-growing organization focused on providing employment background screening solutions and building trusted relationships with our clients. 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.

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

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
• Able 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
• CICD, containerization, infrastructure automation, and cloud deployment patterns
• 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
• Experience with AI-native PaaS cloud services such as AWS Bedrock, AWS AgentCore, Azure AI Foundry, and Azure OpenAI
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• Experience with LLM ecosystems and providers such as OpenAI, Anthropic