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Founding AI Engineer - Business Automation

Blueline Search Group
Full-time
Remote
United States
$140,000 - $175,000 USD yearly
Artificial Intelligence

Founding AI Engineer — Business Automation

Location: Metro DC; Atlanta, GA; Raleigh, NC; Burlington, VT; or Remote/Hybrid
Type: Full-time - Mostly remote, but closer to a physical location is ideal.
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 + Bonus

About our client

The company we are recruiting for 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, they’ve combined deep domain expertise with a people-first culture built on agility, respect, and trust.
They’re investing heavily in technology to transform how assurance and tax services are delivered so their 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 with multiple model providers (e.g., AWS Bedrock, OpenAI, Gemini, Grok) and a point of view on how to choose the right model for the job.
  • Strong programming skills in Python (preferred) and/or TypeScript/Node.js for backend services, including API design, testing, and integration.
  • Experience with embeddings and vector search (e.g., OpenSearch, pgvector, or dedicated vector databases) and building retrieval pipelines with evaluation and monitoring.
  • Familiarity with security, privacy, and compliance considerations for sensitive data (financial, insurance, healthcare, or similar regulated domains).
  • Practical CI/CD experience (Git-based workflows, automated testing, deployment pipelines) and comfort with infrastructure-as-code (Terraform, AWS CDK, or similar).
  • Comfortable operating as a first-of-its-kind hire: setting standards, making build-vs-buy decisions, and delivering under ambiguity with limited pre-existing infrastructure.
  • Excellent communication skills with non-technical stakeholders; able to facilitate discovery sessions and explain trade-offs in plain language.
  • Bachelor’s degree in Computer Science, Engineering, or a related field (or equivalent practical experience).


 

Nice to Have

  • Background or experience with audit, tax, insurance, or nonprofit organizations.
  • Experience designing or operating agent frameworks and workflow orchestrators
(e.g., multi-step agent graphs, tool-calling frameworks, evaluator agents).
  • Experience fine-tuning or adapting models (instruction tuning, RAG-first design, lightweight adapters) in a production context.
  • Familiarity with data engineering patterns and working with messy real-world data (Excel, PDFs, unstructured documents).
  • Experience building simple front-end interfaces (e.g., 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 (Outcomes & Measures) first 90 Days
  • Develop a clear understanding of our core audit and tax workflows and identify 1–2 high-impact AI opportunities with business stakeholders.
  • Propose and align on a reference architecture for AI/agentic systems on AWS, including security, logging, and evaluation.
  • Deliver at least one pilot AI capability (e.g., document summarization assistant, checklist generator, or workpaper drafting helper) to a limited group of users.
 

By 6 Months

 
  • Launch 2–3 AI features or agents in production that are used regularly by engagement teams.
  • Demonstrate measurable reduction (e.g., 20–30%) in time spent on targeted tasks through automation/AI augmentation.
  • Establish evaluation and guardrail frameworks (prompt testing, offline/online evaluation, safety checks) adopted across AI initiatives.
 

By 12 Months

 
  • Help hire and mentor at least one additional engineer or technical contributor focused on AI/automation.
  • Publish internal patterns, templates, and best practices for building AI features, enabling other teams to move faster with confidence.
  • Demonstrate meaningful business impact e.g., improved margins, faster turnaround times, or new client-facing value-add services attributable to AI capabilities.

How We Work

Our culture values agility, respect, and trust. We:
 
  • Work in short, iterative cycles with tight feedback loops from end users.
  • Document what we build so that others can understand, reuse, and extend it.
  • Favor modern, open, and maintainable solutions over complex, fragile stacks.
  • Treat governance and risk management as enablers, not blockers, of innovation especially when working with client data.