Lead AI Engineer

Accurate Background

7d•$121,000 - $185,000•Hybrid

About The Position

When you join Accurate Background, you’re an integral part of making every hire the start of a success story. Your contributions will help us fulfill our mission of advancing the background screening experience through visibility and insights, empowering our clients to make smarter, unbiased decisions. 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 Engineer 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, and scalable AI solution architectures. The Lead AI Engineer will help transform the traditional SDLC into an AI Development Lifecycle (AI-DLC), materially changing how solutions are designed, built, evaluated, deployed, and continuously improved. This role focuses on delivering AI-powered solutions that drive operational efficiency, improve experiences, 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
8+ years of cloud software engineering and/or AI engineering experience
8+ years of programming experience in Python
Hands-on experience building AI-enabled applications, GenAI solutions, or AI agents.
Experience with AWS and/or Azure, including AI-native PaaS cloud services (e.g., AWS Bedrock, AWS AgentCore, Azure AI Foundry, Azure OpenAI)
Strong understanding of GenAI patterns: 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
Experience with agent frameworks and orchestration tools (LangGraph, Semantic Kernel etc.)
Experience with LLM ecosystems and providers (OpenAI, Anthropic, Llama, Mistral, etc.)
Experience with vector databases and retrieval systems (Pinecone, Azure AI Search, etc.)
Experience with tool/agent AG-UI, A2A, MCP interoperability protocols, registries, and reusable service layers
Experience integrating APIs and enterprise systems into AI workflows
Solid understanding of modern architecture patterns (microservices, APIs, event-driven systems)
Experience working in Agile/Scrum environments
Understanding AI governance, responsible AI, and compliance considerations
Ability to translate business problems into AI solutions
Strong analytical and problem-solving skills
Self-starter with strong collaboration and communication skills

Nice To Haves

Experience building or contributing to an AI Center of Excellence, AI platform, or AI innovation team.
Experience defining or implementing an AI Development Lifecycle (AI-DLC).
Experience implementing structured Context Engineering practices for improving model reliability and consistency
Experience with observability and evaluation tools (OpenTelemetry, Datadog, CloudWatch, LangSmith etc.)
Experience with CI/CD, containerization, and infrastructure automation
Experience in regulated, compliance-driven environments
Experience delivering AI solutions for operational automation, customer experience, or revenue-generating products
Familiarity with AI governance frameworks (e.g., ISO/IEC 42001, NIST AI RMF)
AI Mindset: Balance rapid experimentation with disciplined engineering practices, Design for scalability, reusability, and governance from the start, Focus on measurable business outcomes and value creation, Embrace ambiguity and help define standards for emerging AI capabilities, Build systems with continuous evaluation, monitoring, and improvement, Apply responsible AI principles across all solutions

Responsibilities

Design, build, test, deploy, and maintain AI-enabled solutions including AI agents, tools, and GenAI-powered applications
Establish foundational AI CoE standards, reusable patterns, and engineering guardrails.
Contribute to development of an AI lab for experimentation, prototyping, evaluation, and rapid iteration
Define and implement AI-DLC practices including experimentation, evaluation, governance, deployment, and monitoring
Build reusable and scalable AI platforms, services, APIs, and orchestration patterns
Identify and deliver AI use cases that drive operational efficiencies and business value
Apply GenAI patterns such as RAG, prompt engineering, agent orchestration, tool calling, and human-in-the-loop workflows
Develop AI agents, tool integrations, and orchestration workflows across enterprise systems
Work with LLMs, SLMs, embeddings, vector search, and model APIs
Create technical designs, reusable frameworks, and implementation standards
Implement secure, observable, and maintainable Python-based AI services
Establish and follow AI governance practices including responsible AI, data protection, and compliance
Support build, deployment, monitoring, and troubleshooting of AI applications
Develop evaluation frameworks, quality benchmarks, and feedback loops
Partner with stakeholders to move AI ideas from concept through production and scale
Mentor engineers on AI engineering practices and AI-native development