Machine Learning Engineer

Key Responsibilities

Prepare, clean, transform, and validate datasets for ML experiments.

Develop and evaluate baseline machine learning models.

Write maintainable Python code using pandas, NumPy, and scikit-learn.

Apply ML evaluation metrics and identify issues such as overfitting.

Assist with integrating models into applications, APIs, and data pipelines.

Participate in code reviews, testing, debugging, and documentation.

Collaborate with senior engineers and cross-functional teams.

Communicate progress, raise blockers early, and apply feedback.

Must-Haves

Ideally 6–12 months of relevant professional, internship, co-op, or equivalent hands-on experience.

Maximum 2 years of total experience — candidates exceeding this are not eligible.

Foundational supervised and unsupervised machine learning knowledge.

Python, pandas, NumPy, and scikit-learn.

Basic SQL and structured data handling.

Fundamentals of Git, testing, debugging, and modular coding.

Strong communication, adaptability, and willingness to learn.

Nice-to-Haves

Academic or internship projects covering data preparation through model evaluation.

Exposure to deep learning or generative AI.

REST APIs, cloud platforms, notebooks, containers, or CI/CD.

Git pull requests and code reviews.

Responsible AI concepts, including bias, privacy, and explainability.