Description :

We are seeking a highly motivated and enthusiastic AI Engineer to join our growing Artificial Intelligence team at DataFlow Group. This entry-level role is perfect for a recent graduate or early-career professional who is passionate about machine learning, deep learning, and building innovative AI-driven solutions. The successful candidate will work closely with senior engineers and data scientists to implement, test, and maintain machine learning models and pipelines.

Responsibilities :

- Assist in the collection, cleaning, and preprocessing of large datasets for ML model training.

- Implement, test, and tune various machine learning algorithms, including Regression, Classification, and Clustering techniques, and prototypes under guidance.

- Work on specific domain models such as Computer Vision, Image Processing, and Natural Language Processing (NLP).

- Write and maintain clean, efficient, and well-documented code primarily in Python.

- Participate in the deployment and monitoring of ML models in production environments.

- Conduct basic statistical analysis, A/B testing, and comprehensive model evaluation.

- Stay up-to-date with the latest AI/ML research and technologies.

- Collaborate with cross-functional teams (e.g., software engineering, data science) to integrate AI/ML solutions.

Minimum Qualifications :

- Bachelor's degree in Computer Science, Engineering, AI, or a related field.

- Familiarity with data structures, algorithms, and software design principles.

- Proficiency in Python and experience with core ML libraries (e.g., Scikit-learn, Pandas, NumPy).

- Familiarity with at least one deep learning framework (e.g., TensorFlow or PyTorch etc).

- Solid understanding of basic machine learning concepts (e.g., supervised vs. unsupervised learning, model

evaluation metrics, cross-validation, and hyperparameter tuning).

- Basic knowledge of SQL and database concepts.

- Excellent problem-solving skills and a willingness to learn rapidly.

Preferred Qualifications :

- Prior internship or project experience in AI/ML, demonstrating practical feature engineering or model development skills.

- Experience with version control systems (e.g., Git).

- Exposure to cloud platforms (e.g., AWS, Azure, GCP) and containerization (e.g., Docker) for potential ML deployment.

- Demonstrated ability to read, understand, and summarize complex research papers. Evidence of academic

publications.