Job Description

Purpose

This opportunity is for an independent contractor. We are seeking a highly experienced COBOL professional to serve as a consultant on AI training data projects for leading AI model builders and enterprises. Your focus will be to define success criteria, review outputs, and provide targeted guidance to improve quality and speed — directly contributing to the successful delivery of domain-specific annotated datasets and code samples that meet the highest technical standards. You will be engaged on specific projects with clearly defined deliverables, milestones, and end dates.

Components

Technical Standard Setting, Quality Control, and Process Improvement

• Define COBOL-specific quality success metrics for code annotation and dataset labeling projects.
• Develop project-specific SOPs, QA rubrics, and reference materials to ensure outputs align with client technical standards.
• Review project outputs (COBOL scripts, code annotations, legacy modernization samples) against defined standards, flagging and correcting defects before client delivery.
• Conduct structured QA passes on deliverables; track, flag, and resolve defects efficiently to hit delivery timelines.
• Return work to contractors with precise remediation notes and context on COBOL syntax, logic, and legacy system patterns.
• Provide advisory input on tools, frameworks, emulators, and workflow improvements to maintain quality benchmarks in mainframe and batch-processing environments.
• Handle spec changes and edge-case scenarios (e.g., different COBOL dialects, EBCDIC vs ASCII encoding, JCL dependencies) and draft corresponding acceptance criteria or workarounds.
• Curate libraries of “gold standard” COBOL code samples, modernization examples, and dataset annotations for calibration and cross-project consistency.

Talent Vetting & Output Improvement

• Participate in technical assesments of contractor talent, including reviewing COBOL code assessments and task-based evaluations.
• Review sample outputs from contractors and provide clear, actionable written feedback to improve code correctness, readability, and efficiency.
• Develop targeted training and calibration resources, such as:
• COBOL code quality guidelines (e.g., data division consistency, paragraph structuring)
• Best practices for clean, maintainable procedural code
• Reference documentation for legacy system interaction patterns
• Dataset labeling standards for COBOL-related model training

Project Delivery Support

• Advise on technical scoping and requirements during project setup, including COBOL versioning, JCL integration, and mainframe data formats.
• Provide expert guidance for edge cases and spec changes, such as handling copybooks, variable-length records, or integration with DB2 and VSAM.
• Contribute to post-project reviews to capture lessons learned and continuously refine standards.
• Identify and summarize client system insights, such as recurring syntax issues, logic errors, or data formatting inconsistencies.
• Build dashboards or defect trackers with categorized issues to surface recurring themes and drive process improvements.
• Conduct post-mortems to analyze defect trends and propose updated QA steps, documentation improvements, or refresher training.

Target Profile

• 5+ years of professional experience in COBOL programming, mainframe systems, or enterprise application maintenance, with demonstrable project impact.
• Mastery of COBOL (and familiarity with JCL, DB2, CICS, or VSAM preferred).
• Proven ability to define, enforce, and maintain high technical standards in legacy system environments.
• Strong communication skills, with the ability to convey technical feedback clearly to both engineers and non-technical reviewers.
• Experience developing technical documentation, QA frameworks, or training content for legacy or mainframe systems.
• Ability to deliver against fixed project timelines and scopes.
• Exceptional attention to detail and a commitment to accuracy, consistency, and documentation discipline.
• Fluency in spoken and written English with clear, concise technical writing.