Machine Learning Engineer

The Machine Learning Engineer will support the design, development, testing, and optimization of machine learning models and data-driven solutions. The role includes assisting with data preparation, model training, algorithm evaluation, and performance monitoring under the guidance of experienced technical supervisors. The participant will gain practical exposure to machine learning workflows, software engineering practices, data pipelines, and model deployment processes.

Responsibilities may include analyzing datasets, preparing training data, building and refining predictive models, testing model accuracy, documenting technical processes, and supporting integration of machine learning solutions into business or engineering applications. The ideal candidate should have a strong foundatio

Position Requirements: Bachelor’s degree or higher in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Engineering, Mathematics, Statistics, or a related field. Strong foundation in programming, data structures, algorithms, statistics, and machine learning concepts. Proficiency in Python and experience with SQL, TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy, or similar tools preferred. Ability to clean, analyze, and interpret datasets, support model training and testing, evaluate performance, and document technical findings. Strong problem-solving skills, attention to detail, and ability to collaborate with technical and business teams.

To be provided

J-1 Visa sponsorship is available for up to 18 months for qualified candidates interested in training in the U.S. at approved host employer locations.

Position Requirements: Machine Learning Engineer

Education:
Bachelor’s degree or higher in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Engineering, Mathematics, Statistics, or a related technical field.

Technical Knowledge:
Strong understanding of machine learning concepts, algorithms, data structures, statistics, probability, and software engineering principles.

Programming Skills:
Proficiency in Python is strongly preferred. Experience with SQL, Java, C++, R, or similar programming languages is a plus.

Machine Learning Tools:
Experience or academic training with machine learning libraries and frameworks such as TensorFlow, PyTorch, Scikit-learn, Keras, Pandas, NumPy, or similar tools.

Data Skills:
Ability to collect, clean, organize, analyze, and interpret structured and unstructured datasets.

Model Development:
Basic experience with model training, testing, validation, performance evaluation, and optimization.

Software & Cloud Exposure:
Familiarity with APIs, Git/GitHub, cloud platforms, databases, data pipelines, or deployment environments is preferred.

Analytical Skills:
Strong problem-solving ability, attention to detail, and capacity to translate data insights into practical technical solutions.

Communication Skills:
Ability to document technical findings, explain model performance, and collaborate with engineering, product, and business teams.

Professional Qualities:
Reliable, organized, eager to learn, able to follow technical guidance, and capable of completing assignments within defined timelines.