Junior Machine Learning Engineer

What role will you play in the team

As a Junior Machine Learning Engineer within our advanced artificial intelligence division, you will collaborate closely with senior data scientists and MLOps engineers. Your primary objective will be to design, develop, and deploy robust predictive models that drive automated decision-making processes across our enterprise data ecosystem, ensuring high-performance scalability and operational reliability.

What you will do

You will be responsible for translating complex business requirements into high-performing algorithmic solutions. Your daily activities will involve data ingestion, feature engineering, model training using scikit-learn and PyTorch, and deploying RESTful inference endpoints into production environments while monitoring data drift and model accuracy metrics continuously.

Key responsibility

• Design and implement scalable machine learning pipelines using Python to automate model training and validation workflows efficiently.
• Perform extensive exploratory data analysis on massive structured and unstructured datasets to extract actionable engineering features.
• Optimize deep learning architectures utilizing TensorFlow and PyTorch for enhanced inference speed and reduced memory consumption.
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  Collaborate with data engineering teams to integrate SQL and MongoDB database queries seamlessly into the ML pipeline architecture.
• Conduct rigorous hyperparameter tuning and cross-validation experiments to maximize predictive accuracy and minimize overfitting risks.
• Document all model development phases, algorithmic assumptions, and version control procedures rigorously within enterprise repositories.

Required Qualification and Skills

• Bachelor's or Master's degree in Computer Science, Statistics, Artificial Intelligence, or a closely related quantitative discipline.
• Demonstrated hands-on programming proficiency in Python along with advanced querying capabilities in SQL and MySQL.
• Practical project experience leveraging machine learning frameworks such as scikit-learn, TensorFlow, and PyTorch for predictive modeling.
• Strong foundational knowledge of statistical analysis, probability theory, linear algebra, and evaluation metrics like ROC-AUC and F1-score.
• Familiarity with big data processing tools like Apache Spark and Hadoop for handling large-scale distributed datasets.

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  Excellent collaborative problem-solving skills coupled with clear technical communication abilities for cross-functional project execution.

Benefits Included

• Comprehensive health, dental, and vision insurance coverage ensuring total wellness security for you and your dependents.
• Generous annual paid time off, sick leave, and casual leaves designed to promote an optimal work-life balance.
• Annual professional development stipend dedicated to attending data science conferences, certifications, and advanced training workshops.
• Dynamic, collaborative, and inclusive startup-culture office environment equipped with modern recreational facilities and snacks.
• Performance-based quarterly bonuses and equity options aligned with corporate growth milestones and project delivery success.

A Day in the Life

Your morning begins by reviewing model performance logs from overnight training runs on our cloud cluster. You then participate in an agile daily standup to align on feature engineering sprints. The afternoon is dedicated to writing Python scripts for deep learning architectures, optimizing database queries, and code-reviewing peer pull requests before wrapping up your day.