Atique A.

Key Skills

Software

AWS SageMaker

Other

CVAT

Data Annotation Tech

Dataloop

Encord

Google Cloud Vertex AI

Labelbox

LabelImg

Label Studio

Mercor

Micro1

Mindrift

Playment

Prodigy

Redbrick AI

Remotasks

Appen

Top Subject Matter

Machine Learning

model training/deployment for real-time inferencing systems

Deep Learning (CV/NLP)

Top Data Types

Image

Text

Document

Top Task Types

Fine-tuning

Classification

Bounding Box

Polygon

Segmentation

Entity (NER) Classification

Point/Key Point

Cuboid

Object Detection

Text Generation

Question Answering

Text Summarization

RLHF

Transcription

Evaluation/Rating

Computer Programming/Coding

Data Collection

Function Calling

Prompt + Response Writing (SFT)

Freelancer Overview

Machine Learning Engineer II. Brings 7+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include AWS SageMaker, Other, and MLflow. Education includes Master of Science, National University of Sciences & Technology (2022) and Bachelor of Science, FAST National University of Computer & Emerging Sciences (2019). AI-training focus includes data types such as Computer Code and Programming and labeling workflows including Fine-tuning and Classification.

Labeling Experience

Machine Learning Engineer II

AWS SageMakerFine-tuning

Led development and optimization of machine learning pipelines for real-time business applications, focusing on automated training and evaluation workflows. Implemented automated model training, assessment, and deployment using SageMaker Pipelines, and established mechanisms for continuous retraining via drift detection. Built and deployed ML inference services with monitoring to support production-grade model updates. • Automated training/evaluation/deployment using AWS SageMaker Pipelines • Built model drift detection using data snapshots and AWS Step Functions for automated retraining • Deployed real-time inferencing APIs with scaling and latency monitoring (Lambda/CloudWatch) • Implemented CI/CD automation with GitHub Actions for testing, linting, and deployment

2024 - Present

Deep Learning Engineer

OtherFine-tuning

Led deep learning initiatives across computer vision and NLP, using transformer and CNN-based architectures to train and deploy models. Deployed fine-tuned models via TorchServe and TensorFlow Serving integrated with AWS EC2 and S3 for production inference. Optimized training workflows for GPU efficiency and improved serving performance through quantization and ONNX conversion. • Fine-tuned deep learning models and deployed with TorchServe/TensorFlow Serving • Built GPU-optimized training using mixed precision and data parallelism • Integrated interpretability tools (Grad-CAM, SHAP) into production-facing models • Reduced model serving latency using quantization and ONNX pipelines

2023 - 2024

Machine Learning Engineer I

Fine-tuning

Developed scalable machine learning solutions for OCR, document classification, and sentiment analysis with a focus on training and experiment management. Implemented hyperparameter tuning using Optuna and tracked runs in MLflow to support iterative model improvement. Designed augmentation and monitoring workflows to improve model performance in low-data computer vision settings. • Performed hyperparameter tuning (Optuna) and experiment tracking (MLflow) • Built custom data augmentation pipelines for low-data computer vision tasks • Developed Jupyter dashboards for model performance monitoring and visual debugging • Automated linting/testing/packaging of model builds using GitHub Actions

2022 - 2023

Machine Learning Engineer

OtherClassification

Designed and trained multiple supervised ML systems including recommender systems, binary classifiers, and time-series forecasters. Implemented model explainability for financial use cases using SHAP and LIME with audit requirements. Orchestrated model deployment through Docker and CI/CD workflows and improved inference latency by migrating from ensembles to distilled architectures. • Trained binary classifiers and time-series forecasters using robust validation • Implemented explainability (SHAP, LIME) for audit-compliant financial models • Deployed models with Docker and GitHub Actions CI/CD pipelines • Reduced inference latency by switching to distilled architectures

2021 - 2022

Education

Work History