Full-Stack AI Developer
Role: Full Stack AI Engineer
Location: Charlotte NC (hybrid)
Employment Type: Fulltime
Position Summary:
Qualifications:
Key Responsibilities:
Build AI-Powered Internal Tools
Architect and Scale AI Foundations:
Automate Operational Workflows:
Build systems that follow structured pipelines:
Integrate AI Into Core Business Systems:
Build Agent & Tool Execution Systems:
Cross-Functional Collaboration:
Location: Charlotte NC (hybrid)
Employment Type: Fulltime
Position Summary:
- We are seeking a Full Stack AI Engineer who combines strong software engineering fundamentals with applied AI creativity. This role plays a foundational part in shaping the companys AI and automation strategyarchitecting, building, and deploying intelligent tools that transform how the business operates.
- You will own full-stack development of AI-driven internal tools, partner directly with operators and business units, and help build an internal culture of AI adoption. This role requires high ownership, strong execution, and a passion for building practical, real-world AI solutions.
Qualifications:
- 47+ years of professional experience in software engineering with modern web frameworks.
- Strong Python experience in production environments.
- Experience shipping applied LLM features into production (not just prototypes), including summarization, extraction, classification, routing, or operator-assist workflows.
- Experience building RAG systems end-to-end including document ingestion pipelines, chunking strategy iteration, retrieval, and context assembly.
- Experience building agent or tool-calling workflows where models trigger tools or actions with clear contracts and safety boundaries.
- Experience delivering software used by real operators including review flows, exception handling, auditability, and measurable improvement.
- Strong communication and problem translation skills with the ability to turn ambiguous operational needs into deployable workflows with clear success metrics.
- Experience integrating with call center or transcript systems is preferred.
- Experience integrating with CRM or ERP platforms is preferred.
- Experience with evaluation practices for LLM systems such as sampling strategies, rubric-based scoring, regression checks, and prompt versioning is preferred.
- Strong REST API design experience, with versioning best practices preferred.
- Experience with asynchronous processing and pipeline-style workloads.
- Experience building internal or administrative user interfaces (React experience preferred).
- Familiarity with cloud deployment, CI/CD pipelines, and environment configuration.
Key Responsibilities:
Build AI-Powered Internal Tools
- Design, prototype, and deploy full-stack AI applications using Python frameworks (FastAPI/Django) and modern front-end frameworks (React/Next.js).
- Develop AI-powered capabilities including summarization, classification, routing, extraction, and workflow automation.
- Build operator-facing tools such as review queues, exception handling, and traceability views to ensure AI outputs are trusted and usable.
Architect and Scale AI Foundations:
- Design and implement reliable retrieval-augmented generation (RAG) systems including ingestion pipelines, embeddings, vector search, and context assembly.
- Build scalable patterns for APIs, cloud deployment, CI/CD, and AI service orchestration.
- Anticipate and mitigate common LLM failure modes including irrelevant retrieval, missing context, hallucinated outputs, and stale data.
Automate Operational Workflows:
- Identify manual workflows across operations, sales, finance, and field teams and replace them with AI-enabled automation.
Build systems that follow structured pipelines:
- ingestion retrieval agent/tool execution human review measurement
- Design human-in-the-loop workflows with clear escalation paths, editable outputs, and feedback capture.
Integrate AI Into Core Business Systems:
- Build integrations with operational systems including ServiceTitan, call center platforms, and internal data systems.
- Develop transcript-driven workflows such as call summaries, lead qualification insights, automated tagging, and routing.
- Ensure workflows operate reliably within real-world constraints including messy data, timing dependencies, and operational handoffs.
Build Agent & Tool Execution Systems:
- Develop agent-based workflows that trigger tools to perform operational actions such as updating records, retrieving context, or triggering workflows.
- Implement safe tool-calling patterns including defined input/output contracts, validation, retries, and permission scoping.
- Ensure systems remain observable through logging, intermediate traces, and measurable outcomes.
Cross-Functional Collaboration:
- Partner with operators, engineering teams, and leadership to identify high-impact AI opportunities.
- Translate ambiguous business problems into deployable AI workflows.
- Communicate technical concepts clearly to non-technical stakeholders and support AI adoption across the organization.
