AI Developer

AI Developer

Location: Charlotte, NC

Interview: Face-to-Face / Onsite

Experience: 5–10 Years

Work Arrangement: Onsite in Charlotte, NC

Job Summary

We are seeking a highly skilled AI Developer to join our team in Charlotte, NC . The AI Developer will design, develop, integrate, and optimize intelligent document-processing solutions that modernize Middle Office authorization workflows .

The ideal candidate will have strong hands‑on experience with Python, AI/ML, LLMs, Intelligent Document Processing (IDP), OCR, document extraction, confidence scoring, validation, semantic comparison, REST APIs, microservices, Kafka, LangChain, Pydantic, and LangGraph .

This role requires an engineer who can work across the complete document intelligence lifecycle—from document ingestion and classification through extraction, validation, exception handling, comparison, and downstream system integration.

Candidates must be available for a face‑to‑face interview and onsite work in Charlotte, NC.

Key Responsibilities

Document Ingestion & Classification

Design and develop document ingestion pipelines for Authorization Certificates and non‑standard documents .

Build document classification workflows capable of identifying different document types and formats.

Process PDFs, scanned documents, structured forms, and semi‑structured/unstructured content.

Integrate OCR and document-processing technologies into scalable AI pipelines.

Handle document quality issues including poor scans, missing fields, inconsistent formats, and variations in templates.

Document Extraction & AI/LLM Development

Implement template‑based and rule‑based extraction for structured documents and standardized forms.

Develop AI/LLM-based extraction solutions for unstructured and semi‑structured documents.

Use Python and modern AI frameworks to develop reliable document intelligence applications.

Develop prompts, extraction schemas, validation rules, and processing workflows.

Use LangChain, Pydantic, and LangGraph to build structured AI workflows and agentic/document-processing pipelines.

Design reusable extraction components that can support multiple document types.

Confidence Scoring & Validation

Develop field‑level confidence scoring for extracted information.

Implement record‑level confidence scoring and quality assessment.

Establish validation thresholds and automated exception‑handling processes.

Create rule‑based validation mechanisms to identify inaccurate, incomplete, or suspicious extracted information.

Develop fallback mechanisms for low‑confidence AI results.

Ensure extracted information can be traced back to the original source document.

AI-Based Comparison & Verification

Develop AI‑powered comparison capabilities between:

Extracted data and source documents.

Extracted records and existing system data.

Contact Master data and source documentation.

Implement semantic, fuzzy, and rule‑based comparison logic .

Identify discrepancies, mismatches, missing information, and potential data‑quality issues.

Develop explainable comparison results that clearly indicate why a record was accepted, rejected, or sent for manual review.

API & Microservices Integration

Design and develop REST APIs and microservices for document‑processing and AI capabilities.

Integrate AI/ML services with enterprise applications and workflow platforms.

Develop JSON/XML schema mappings between systems.

Integrate event‑driven processing using Kafka .

Support downstream system integrations and automated workflow processing.

Troubleshoot integration, API, data‑mapping, and processing issues.

Exception Handling & Workflow Automation

Design automated workflows for handling low‑confidence and exception scenarios.

Route documents requiring human review to appropriate workflow queues.

Develop mechanisms for manual review and feedback incorporation.

Integrate AI capabilities with enterprise workflow and automation platforms.

Support BOT/automation integration for downstream processing where required.

Security, Compliance & Auditability

Implement appropriate controls for PII and sensitive document data .

Follow enterprise security and data‑governance standards.

Design AI solutions with explainability and traceability in mind.

Maintain audit trails for document processing, extraction, validation, and human overrides.

Ensure AI‑generated results can be reviewed and supported by source‑document evidence.

Follow applicable regulatory and compliance requirements for financial‑services environments.

Required Qualifications

Bachelor’s degree in Computer Science, Engineering, Artificial Intelligence, Data Science, or a related technical field.

3–6 years of professional experience in AI/ML, Intelligent Automation, Document AI, or related engineering roles.

Strong hands‑on programming experience with Python .

Experience developing production‑grade AI/ML or intelligent document‑processing solutions.

Hands‑on experience with OCR, PDF parsing, Document AI, Intelligent Document Processing (IDP), or document extraction platforms .

Experience implementing confidence scoring and validation logic.

Strong understanding of semantic comparison, fuzzy matching, and rule‑based comparison.

Experience developing and integrating REST APIs and microservices .

Strong understanding of JSON/XML schema mapping.

Experience with Kafka or event‑driven architectures.

Experience handling structured, semi‑structured, and unstructured documents.

Technical SkillsRequired

Python

LangChain

Pydantic

LangGraph

LLM / Generati

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