We are looking for a motivated Junior Machine Learning Engineer to join our AI & Data Science team. In this role, you will help design, implement, and scale machine learning solutions under the guidance of senior engineers and scientists. You will work on model development, deployment, and monitoring within production-grade systems. This is an excellent opportunity to gain hands-on experience with real-world ML pipelines and contribute to impactful, data-driven solutions.
The ideal candidate has a strong foundation in computer science and machine learning principles and is eager to grow into a production-focused ML engineering role.
Support the development and training of machine learning models using structured and unstructured datasets.
Assist in building and maintaining data pipelines for feature engineering and model input processing.
Collaborate with data scientists, engineers, and product managers to integrate ML models into applications.
Help monitor model performance and implement retraining or tuning strategies as needed.
Write clean, modular, and well-documented code following engineering best practices.
Participate in code reviews and contribute to improving team workflows and tooling.
Stay up to date with emerging ML tools, frameworks, and techniques.
Education:
Bachelor’s degree in Computer Science, Machine Learning, Artificial Intelligence, Data Science, or a related technical field.
Experience:
0–2 years of experience in machine learning, software engineering, or relevant internships/projects.
Proficiency in Python and ML libraries such as Scikit-learn, TensorFlow, or PyTorch.
Solid understanding of core ML concepts (e.g., regression, classification, overfitting, cross-validation).
Experience working with SQL and data manipulation libraries (Pandas, NumPy).
Familiarity with APIs, containerization (Docker), and version control systems like Git.
Basic knowledge of ML lifecycle management and deployment principles.
Strong debugging, troubleshooting, and critical thinking skills.
Ability to work collaboratively and communicate technical concepts clearly.
Exposure to cloud platforms (e.g., AWS Sagemaker, GCP Vertex AI, Azure ML).
Understanding of CI/CD pipelines and MLOps concepts.
Experience with experiment tracking tools (e.g., MLflow, Weights & Biases).
Familiarity with REST APIs, microservices, or model-serving frameworks (e.g., Flask, FastAPI, TorchServe).