- Computer Vision : Image processing, Morphological transformations. Familiarity with Object detection/classification/tracking. Knowledge of Optical character recognition, relevant tools include Tesseract, easyOCR.

- AI model development : Knowledge of deep learning frameworks such as TensorFlow and Pytorch.

- Tool Knowledge : Familiarity with popular open-source AI frameworks such as OpenAI and Microsoft CoPilot.

Software Deployment :

- REST API : Host the AI/ML model through Django REST API.

- Cloud : Familiarity with tools such as AWS EC2, Lambda and S3. Data security concepts such as IAM authentication.

- Version Tracking : Code and Data version tracking through GitHub/Gitlab.

- Code Development : Strictly follow the principle of Modularity, code-readability and code-transferability.

- Documentation : Documenting the progress in a comprehensible way to assist future references.

Data Engineering (Good to have but not necessary) :

- Data Pipelines : Develop modular and efficient ETL (Extract Load and Transform Pipelines). Orchestrate the pipelines through tools such as Dagster/Airflow. Ability to handle both structured and unstructured data.

- Data Warehousing : Normalization/Denormalization, Partitioning. Data storage and querying techniques to avoid any memory issues.

- Data Visualization : Develop data UI applications using Django. Familiarity with tools such as PowerBI, Tableau.

- Data Analysis : Tabular data analysis through SQL, Pandas, Polars etc.

- Predictive Analysis : Develop machine learning predictive models using Scikit-learn, Scipy etc.

- Big Data Processing : Familiarity with big data frameworks such as Apache spark/Pyspark.

Ideal Candidate Profile :

Qualifications & Experience :

- Bachelors/masters degree in computer science, Data Science, Statistics, Engineering, or a related field.

- 2-5 years of experience in data science, engineering, and visualization.

- Strong experience in data analytics, AI/ML, and business intelligence tools.