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Z
Zakir

Zakir

ML Systems & AI Evaluation Engineer

India flagKadapa, India

Key Skills

Software

Data Annotation TechData Annotation Tech
Don't disclose

Top Subject Matter

LLM Evaluation & AI Reasoning
AI Code Evaluation & RLHF
ML Systems & Inference Optimization

Top Data Types

Computer Code ProgrammingComputer Code Programming
TextText
DocumentDocument

Top Task Types

Computer Programming/CodingComputer Programming/Coding
RLHFRLHF
Evaluation/RatingEvaluation/Rating
Text GenerationText Generation
Question AnsweringQuestion Answering
Fine-tuningFine-tuning
Function CallingFunction Calling
Red TeamingRed Teaming

Freelancer Overview

AI Coding Agent Evaluator and ML Systems Engineer with hands-on experience in AI evaluation, structured reasoning analysis, and production-oriented ML infrastructure. Worked on evaluating AI-generated code and ML serving workflows through schema validation, edge-case testing, and failure analysis, including identifying validation-boundary regressions in FastAPI + Pydantic + ONNX Runtime pipelines. Experienced with Python, FastAPI, ONNX Runtime, Pydantic V2, RAG systems, and LLM evaluation workflows. Strong focus on reasoning quality, code correctness, logical consistency, and identifying subtle failure modes in AI-generated outputs through controlled testing and structured evaluation.

Labeling Experience

AI Coding Agent Evaluator (Freelance) — Schema Integrity & Vectorized Inference testing for ML serving pipelines

Don't discloseComputer Code ProgrammingComputer Code ProgrammingRed TeamingRed Teaming

Evaluated reliability of LLM-generated ML serving pipelines via adversarial schema validation and inference-path testing to identify boundary failures and runtime crashes. Authored structured PASS/FAIL reports and analyzed implicit constraint reasoning gaps and missing domain-level test coverage. Designed controlled test harnesses that reproduce validation failures across row-oriented and column-oriented API contracts. • Performed schema integrity testing for Pydantic V2 validation paths • Tracked regression cases where categorical inputs bypass validation and crash ONNX inference at runtime • Created boundary-focused test harnesses for different API contract orientations • Produced structured evaluation outputs (PASS/FAIL) for validation outcomes and failures

2026 - 2026

Education

V

Vellore Institute of Technology

Bachelor of Technology, Computer Science

Bachelor of Technology
2022 - 2026

Work History

F

Freelance

AI Coding Agent Evaluator (Freelance)

N/A
2026