AI Data Scientist

5 days ago

Rawalpindi, Punjab, Pakistan Value Engineering Pakistan Full time 9,000,000 - 12,000,000 per year

Position:
AI Data Scientist & Annotation Expert

Location:
On-Site - Phase-IV, Civic Center, Bahria Town, Rawalpindi

Employment Type:
Full-time

Shift:
Night

How to Apply
: Share your resume via email -

About the Role

We are looking for a skilled
AI Data Scientist & Annotation Expert
to drive the full lifecycle of AI/ML solutions from data collection and annotation through advanced model development, training, and deployment. The role combines hands-on annotation expertise (critical for building high-quality training datasets) with data science and machine learning skills to ensure robust, production-ready AI systems.

You will play a central role in curating high-quality datasets, experimenting with algorithms, and ensuring model performance by leveraging both advanced data science techniques and precise annotation workflows.

Key Responsibilities
:

Data Science & Machine Learning

  • Design, build, and evaluate machine learning/deep learning models for NLP, Computer Vision, or multimodal tasks.
  • Perform feature engineering, exploratory data analysis (EDA), and statistical modeling.
  • Experiment with different model architectures (CNNs, RNNs, Transformers, LLMs) and optimize hyperparameters for maximum performance.
  • Develop and deploy scalable ML pipelines in production (using FastAPI, TensorFlow Serving, TorchServe, or containerized environments).
  • Collaborate with engineers and product teams to integrate models into end-user applications.

Data Annotation & Curation

  • Design annotation workflows and guidelines for various data types (images, PDFs, audio, video, and text).
  • Perform and manage manual and semi-automated annotation tasks (e.g., bounding boxes, polygons, segmentation masks, labeling, and text tagging).
  • Implement quality control measures to ensure annotations meet accuracy and consistency standards.
  • Train and supervise annotation teams, providing feedback and ensuring adherence to project requirements.
  • Work with annotation tools (e.g., Label Studio, CVAT, Supervisely, Prodigy, Labelbox).
  • Assist in building gold-standard datasets that serve as the foundation for AI/ML training.

MLOps & Data Ops

  • Set up pipelines for dataset versioning, experiment tracking, and model reproducibility (using MLflow, DVC, or similar).
  • Monitor dataset drift, class imbalances, and annotation consistency to avoid bias in models.
  • Collaborate with DevOps teams for deployment, scaling, and monitoring of AI models.

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