Client Company Introduction:

An AI-based agriculture technology firm headquartered in Canada.

Job Description:

• Data Preparation & Baseline Model:

Audit and validate annotated datasets for quality and consistency. Define data schema, coordinate systems, and identifiers. Implement preprocessing workflows (tiling, normalization, augmentation). Split data into training, validation, and test sets for robust evaluation. Train baseline object detection or counting models (e.g., YOLO, Faster R-CNN). Deliver baseline performance report with metrics (precision, recall, counting error).

• Model Optimization & Calibration:

Optimize model performance using hyper-parameter tuning and hard example mining. Validate across diverse conditions and unseen data. Implement geospatial alignment and calculate object densities or spatial statistics. Calibrate detection thresholds for precision and recall balance. Compare automated results with manual benchmarks and refine accordingly.

• Cloud Deployment & Output Generation:

Containerize models (Docker) and deploy on AWS or Azure using services like Sage Maker, ECS, or Azure ML. Automate inference pipelines integrated with S3, Blob Storage, or Data Lakes. Generate structured outputs (CSV, GeoTIFF, GeoJSON) for analytics and visualization. Expose APIs for inference job submission and results retrieval. Test scalability and runtime efficiency across sample datasets.

• Reporting, QA, and Knowledge Transfer:

Develop automated reporting tools summarizing predictions and insights (PDF or dashboard format). Conduct end-to-end QA for accuracy, reliability, and coordinate integrity. Document workflows, performance benchmarks, and retraining processes. Provide knowledge transfer and technical training for internal teams.

Required Skills & Experience:

• 5+ years of experience in Machine Learning, AI, or Computer Vision.
• Expertise in Python, PyTorch/TensorFlow, OpenCV, and NumPy/Pandas.
• Proficiency with annotation tools such as Roboflow and CVAT for dataset labeling and management.
• Experience with cloud-based ML deployment on AWS or Azure.
• Strong background in object detection, segmentation, and model optimization.
• Familiarity with geospatial data processing (GDAL, GeoPandas, rasterio, shapely).
• Proficiency with Docker, API development (FastAPI/Flask), and automation scripts.
• Excellent documentation, reporting, and presentation skills.

Other Details:

• Work Mode: Remote from Lahore
• Experience: 5+ Years in AI/ML
• Working Timings: 9am-6pm

About HR Ways:

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