Founding AI Engineer - Business Automation

Founding AI Engineer. Business Automation

Location : Metro DC; Atlanta, GA; Raleigh, NC; Burlington, VT; or Remote/Hybrid
Employment Type : Full time. Mostly remote, though being closer to a physical office location is preferred.
Team: Business Automation, AI-centric solutions
Reports To: Chief Innovation Officer
Travel: 0–10%. Occasional client workshops and internal collaboration.
Compensation: $140,000–$175,000 plus bonus
About Our Client
The company we are recruiting for is a public accounting firm specializing in audit, tax, and advisory services for the insurance and nonprofit sectors, as well as employee benefit plans. For more than 30 years, they have combined deep industry expertise with a people-first culture built on agility, respect, and trust.
They are making a significant investment in technology to modernize how assurance and tax services are delivered. The goal is to free their teams to focus on high-value judgment while providing clients with faster and more insightful outcomes.
About the Team and Role
You will join the Business Automation team, which is responsible for building the next generation of tools that support how the firm works across data, automation, and intelligent systems.
This is the team’s first AI engineering hire and a true greenfield role. There is no legacy codebase and no prebuilt platform. In this role, you will:

• Act as a founding AI engineer and the primary builder of the firm’s AI stack on AWS
• Design and deliver a vision for workforce automation systems that can reason, use tools, and collaborate with people
• Lay the foundation to grow into a technical leadership or management role by mentoring future AI and application engineers

The firm is deeply invested in AWS and works with multiple model providers. These include AWS Bedrock as the primary platform, as well as OpenAI, Gemini, Grok, and others. You will be expected to select and combine the right tools for each use case, balancing risk, cost, performance, and long-term maintainability.
You will also own key technical relationships with AI vendors and partners. This includes working with external partners who have delivered proofs of concept and turning successful experiments into stable, internal capabilities.

What You’ll Do

Design the AI and automation foundation on AWS

• Define reference architectures for LLM and agent-based workloads, including orchestration, retrieval, tools, evaluation, and guardrails
• Use AWS Bedrock alongside external providers such as OpenAI, Gemini, and Grok in a modular, provider-agnostic way

Build AI products that support core audit and tax workflows

• Deliver copilots and automated helpers that assist with drafting workpapers, analyzing documents, summarizing findings, generating testing selections, and preparing client deliverables
• Own the full lifecycle from discovery with domain experts through prototyping, production, monitoring, and iteration based on feedback

Implement robust retrieval and tool-using agents

• Design retrieval pipelines over internal content, structured data, and workpapers with appropriate metadata and access controls
• Build agents that can call tools such as internal APIs, calculators, automations, and workflows while operating within clearly defined boundaries

Establish safety, privacy, and governance practices

• Partner with risk, security, and data teams to ensure responsible handling of client data, including PII controls, redaction strategies, training boundaries, and access management
• Define patterns for prompt hardening, guardrails, and evaluation that align with professional and regulatory obligations

Own vendor and partner relationships

• Serve as the hands-on technical counterpart for AWS, OpenAI, Google, xAI, and other partners
• Evaluate new capabilities, run proofs of concept, and make informed build-versus-buy decisions

Collaborate across teams

• Work closely with data engineering and automation leads to connect AI systems to well-governed data and existing workflows
• Translate loosely defined business problems into clear engineering deliverables and roadmaps

Set engineering standards and mentor future hires

• Establish best practices for AI application development, including testing, observability, CI/CD, experiment tracking, and documentation
• Help hire, onboard, and mentor junior and mid-level engineers as the function grows

Required Qualifications
• 5+ years of experience in software engineering, ML engineering, or applied AI roles, with at least 2+ years focused on LLM- or NLP-centric applications
• Strong experience building secure, production workloads on AWS using services such as Lambda, API Gateway, Step Functions, ECS or EKS, S3, CloudWatch, and IAM
• Proven experience shipping AI or LLM applications to production, including
Retrieval-augmented generation systems over private data
Tool-using or function-calling workflows
Document understanding and extraction pipelines
• Hands-on experience with multiple model providers, such as AWS Bedrock, OpenAI, Gemini, or Grok, and a clear point of view on selecting the right model for a given task
• Strong programming skills in Python, preferred, and or TypeScript or Node.js for backend services, including API design, testing, and integrations
• Experience with embeddings and vector search using tools such as OpenSearch, pgvector, or dedicated vector databases, including evaluation and monitoring of retrieval quality
• Familiarity with security, privacy, and compliance considerations when working with sensitive data in regulated domains such as finance, insurance, or healthcare
• Practical experience with CI/CD, including Git-based workflows, automated testing, and deployment pipelines, as well as infrastructure-as-code tools such as Terraform or AWS CDK
• Comfort operating as a first-of-its-kind hire by setting standards, making architectural decisions, and working effectively with ambiguity
• Strong communication skills and the ability to work with non-technical stakeholders, facilitate discovery sessions, and explain trade-offs in plain language
• Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experienceNice to Have

• Background or experience in audit, tax, insurance, or nonprofit organizations
• Experience designing or operating agent frameworks and workflow orchestration systems, including multi-step agent graphs and tool-calling frameworks
• Experience fine-tuning or adapting models in production through instruction tuning, RAG-first design, or lightweight adapters
• Familiarity with data engineering patterns and working with unstructured or messy real-world data such as Excel files and PDFs
• Experience building simple front-end interfaces, for example using React, to deliver AI capabilities directly to end users
• Prior mentorship or team-lead experience and an interest in building a small team over time

How You’ll Succeed
First 90 Days

• Develop a strong understanding of core audit and tax workflows
• Identify one to two high-impact opportunities for automation in partnership with business stakeholders
• Propose and align on a reference architecture for AI systems on AWS, including security, logging, and evaluation
• Deliver at least one pilot capability, such as a document summarization assistant, checklist generator, or workpaper drafting helper, to a limited user group

By 6 Months

• Launch two to three AI features or automated agents into production that are regularly used by engagement teams
• Demonstrate measurable time savings of approximately 20–30 percent on targeted workflows
• Establish evaluation and guardrail frameworks, including prompt testing, offline and online evaluation, and safety checks, that are adopted across initiatives

By 12 Months

• Help hire and mentor at least one additional engineer or technical contributor focused on automation or AI
• Publish internal patterns, templates, and best practices that enable other teams to build confidently and efficiently
• Demonstrate meaningful business impact, such as improved margins, faster turnaround times, or new client-facing services enabled by these capabilities

How We Work
Our culture is built on agility, respect, and trust. We:

• Work in short, iterative cycles with regular feedback from end users
• Document what we build so others can understand, reuse, and extend it
• Favor modern, open, and maintainable solutions over overly complex stacks
• Treat governance and risk management as enablers of innovation, especially when working with client data