Business and finance professional developing practical expertise in AI training, data annotation, prompt engineering, AI response evaluation, and business & finance domain applications.
A professional profile combining business and finance knowledge with practical, self-directed AI training and evaluation work.
I hold a Bachelor's degree in Accounting and bring professional experience across accounting, auditing, financial analysis, and business operations.
My current professional direction focuses on AI Trainer, AI Data Annotation, Prompt Engineering, and AI Response Evaluation roles.
This portfolio demonstrates how I apply structured evaluation, annotation, prompt iteration, quality assurance, and business & finance domain knowledge to practical AI training tasks. The work is self-directed and designed to demonstrate transferable capability rather than claim prior AI employment.
Practical capabilities demonstrated through the portfolio projects and supporting evidence.
Evaluating AI outputs for accuracy, relevance, clarity, completeness, and instruction following.
Structured labeling and analysis of AI-related datasets using consistent criteria and quality controls.
Designing and iterating prompts with clear objectives, context, constraints, and expected outputs.
Identifying errors, inconsistencies, hallucinations, bias, and other quality issues in AI-generated content.
A collection of 12 practical projects demonstrating AI training, evaluation, annotation, and business & finance applications.
Structured evaluation of AI-generated responses using accuracy, relevance, clarity, completeness, and instruction-following criteria.
Prompt design and optimization through structured iteration, constraints, and comparative evaluation.
Structured labeling and classification using explicit annotation criteria, rationale, confidence, and ambiguity handling.
Domain-specific AI evaluation covering financial accuracy, numerical verification, terminology, assumptions, and business logic.
Checking whether AI outputs satisfy explicit requirements, constraints, format rules, and task objectives.
Identifying potential bias and fairness concerns using structured labels, evidence, and review criteria.
Fact-checking AI-generated claims, identifying unsupported statements, and documenting evidence-based corrections.
Reviewing AI-generated financial analysis for calculations, assumptions, trends, reasoning, and decision relevance.
Finance-focused annotation of structured information using consistent labels, rationale, and quality controls.
Structured QA workflow for detecting defects, classifying root causes, and documenting readiness decisions.
End-to-end workflow design connecting task definition, annotation, evaluation, feedback, QA, and final review.
Dataset quality control covering defect detection, consistency checks, ambiguity, and readiness decisions.
Inspectable evidence supporting the portfolio case studies and demonstrating how the methodologies are applied.
The GitHub portfolio includes structured evaluation records, prompt iterations, annotation tables, financial checks, fact-check records, QA logs, workflow definitions, and data-quality decisions.
Browse All Artifacts Browse EvidenceDomain knowledge supporting specialized AI training, evaluation, annotation, and analysis tasks.
Accounting principles, financial analysis, auditing, financial data, and business reporting.
Business operations, economic concepts, digital economy, and analytical reasoning.
Combining business and finance expertise with structured AI evaluation, annotation, and training workflows.
I am interested in opportunities related to AI training, data annotation, prompt engineering, AI evaluation, and business & finance domain projects.
Direct email: abusakhir44@gmail.com