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# Full Stack Engineer

- **Company:** KeyCorp
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $80,000.0 - $150,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Business Analytics Applications, Automation of Tests, Batch Processing, Cloud Computing, Information Systems, Databases, Content Analysis, Continuous Integration, Data Control, Data Normalization, Information Extraction, Python (Programming Language), Machine Learning, Operational Databases, Cloud Services, SQL Databases, Systems Integration, YAML, Data Processing, Large Language Models, Infrastructure Automation Frameworks, Information Technology, Data Analytics, Data Management, Software Version Control
- **Published:** October 15, 2026
- **Apply:** https://www.builtincolorado.com/auth/login?destination=/job/sr-full-stack-engineer/11688848

## About the Role

* Practical experience with document AI platforms or comparable machine learning services

 * Understanding of supervised model training, labeling strategies, evaluation datasets, confidence thresholds, and error analysis

 * Ability to identify when OCR, rules-based logic, machine learning, or generative AI is the appropriate solution

 * Experience applying large language models to document understanding, unstructured content analysis, summarization, or reasoning use cases

 * Knowledge of responsible AI practices, including explainability, traceability, quality controls, and review escalation

Data and Platform Engineering

 * Experience with cloud object storage, SQL-based analytical platforms, and production data pipelines

 * Batch and event-driven processing patterns, workflow orchestration, retries, monitoring, and operational support

 * Data modeling for extracted values, confidence scores, validation results, and processing history

 * CI/CD, source control, automated testing, environment promotion, and infrastructure-as-code practices

 * Observability and troubleshooting across model, application, data, and integration layers

Business and Delivery Capabilities

 * Ability to translate document-review procedures into clear technical requirements and configurable validation rules

 * Collaboration with business subject-matter experts to define fields, thresholds, exceptions, and acceptance criteria

 * Clear documentation of model behavior, processing logic, decision outcomes, and operational procedures

 * Ability to communicate complex AI and data-processing concepts to technical and non-technical stakeholders

 * Focus on scalable, reusable solutions rather than one-off document automations, * Bachelor's Degree in Information Systems, Computer Science, Data Analytics, or related technical field

 * Hands-on experience delivering document-processing or information-extraction solutions in a regulated industry

 * Experience with sensitive or confidential information and enterprise security controls

 * Familiarity with cloud-based machine learning and data platforms

 * Knowledge of financial-services, legal, healthcare, insurance, or other document-intensive workflows

 * Experience designing exception-based workflows that reduce manual review while preserving quality and control

This position is NOT eligible for employment visa sponsorship for non-U.S. citizens.

## Description

We are seeking a specialist with experience designing, training, and operationalizing intelligent document-processing solutions. The ideal candidate combines applied AI and machine learning expertise with cloud data engineering, secure information handling, and configurable business-rule automation. This role focuses on turning unstructured and semi-structured documents into accurate, auditable, and actionable data products.

Core Technical Skills

 * Intelligent Document Processing (IDP), optical character recognition (OCR), and document classification

 * Training, evaluating, and tuning document extraction models for structured, semi-structured, and handwritten content

 * Entity and field extraction, confidence scoring, quality metrics, and model accuracy assessment

 * Document preprocessing, page-level validation, exception detection, and human-in-the-loop review patterns

 * Python development for data processing, automation, model integration, and reusable components

 * Cloud-native data pipelines using object storage, analytical databases, batch processing, and job orchestration

 * Configuration-driven validation using YAML or similar formats to separate business rules from application code

 * Data normalization, reconciliation, tolerance handling, and comparison against authoritative data sources

 * Encryption, decryption, sensitive-data controls, auditability, and secure cloud processing

 * API and system integration for ingesting documents, publishing results, and supporting downstream workflows