Founding AI Engineer — Business Automation

Atlanta, GA FULL TIME$130,000 – $185,000 About This Role Founding AI Engineer — Business Automation Location: Metro DC; Atlanta, GA; Raleigh, NC; Burlington, VT; or Remote/Hybrid Type: Full-time Team: Business Automation (AI-Centric Solutions) Reports to: Chief Innovation Officer Travel: 0–10% (occasional client workshops and internal collaboration) About Johnson Lambert Johnson Lambert is a public accounting firm focused on audit, tax, and advisory services for the insurance and nonprofit sectors, as well as employee benefit plans. For more than three decades, we’ve combined deep domain expertise with a people-first culture built on agility, respect, and trust. We’re investing heavily in technology to transform how assurance and tax services are delivered—so our teams can spend more time on high-value judgment and our clients can benefit from faster, more insightful outcomes. About the Team & Role You’ll join our Business Automation team, which is chartered with building the next generation of tools that power how we work—data, automation, and AI/agentic systems. This is our first AI engineering hire and a truly greenfield role: no legacy codebase, no pre-baked platform. You will: Act as a founding AI engineer and primary builder of our AI stack on AWS Design and deliver our vision for an agentic workforce—AI systems that can reason, orchestrate tools, and collaborate with our people Lay the groundwork to grow into a management/tech-lead role, mentoring future AI and application engineers We are deeply invested in AWS and use multiple model providers (primarily AWS Bedrock, but also OpenAI, Gemini, Grok, and others). We expect you to select and combine the right tools for each use case—balancing risk, cost, performance, and long-term maintainability. You’ll also be the technical owner of key AI vendor relationships, working with partners who have helped us deliver proofs of concept and turning promising experiments into robust, in-house capabilities. What You’ll Do Design our AI / agentic foundation on AWS Define the reference architecture for LLM and agentic workloads (orchestration, retrieval, tools, evaluation, guardrails) on AWS. Leverage services like Bedrock alongside external providers (OpenAI, Gemini, Grok, etc.) in a modular, provider-agnostic way. Build AI products that transform core audit & tax workflows Deliver copilots and agents that help our people draft workpapers, analyze documents, summarize findings, generate testing selections, and prepare client deliverables. Work end-to-end: discovery with domain experts, prototyping, productionization, monitoring, and iteration based on feedback. Implement robust RAG & tool-using agents Design retrieval pipelines over internal content, structured data, and workpapers with appropriate metadata and access controls. Implement agents that can call tools (internal APIs, calculators, automations, workflows) and operate within well-defined boundaries. Codify safety, privacy, and governance for AI Partner with risk, security, and data teams to ensure responsible use of client data (PII handling, redaction strategies, no-train boundaries, access controls). Establish patterns for prompt hardening, guardrails, and evaluation that reflect our professional standards obligations. Own AI vendor and partner relationships Serve as the hands-on technical counterpart for AWS, OpenAI, Google, xAI, and other partners. Evaluate new capabilities, run proof-of-concepts, and decide when to build vs. buy vs. extend vendor solutions. Collaborate across data, automation, and domain experts Work closely with our Data Engineer and automation leads to connect AI systems to clean, governed data and existing workflows. Translate ambiguous business problems into concrete AI/engineering deliverables and roadmaps. Establish engineering standards & mentor future hires Set best practices for AI application development (testing, observability, CI/CD, experiment tracking, documentation). Help hire, onboard, and mentor junior and mid-level engineers as the AI function grows. Required Qualifications 5+ years in software engineering, ML engineering, or applied AI roles, with at least 2+ years building LLM- or NLP-centric applications. Strong experience building on AWS, including secure production workloads using services such as Lambda, API Gateway, Step Functions, ECS/EKS, S3, CloudWatch, and IAM. Hands-on experience shipping AI/LLM applications to production, such as: Retrieval-augmented generation (RAG) systems over private data Tool-using agents / function-calling workflows Document understanding / extraction pipelines Practical experience wi Apply tot his job