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 actively contribute to the design, training, and deployment of scalable predictive models. You will collaborate closely with senior data scientists and domain experts to transform raw, unstructured datasets into high-performing algorithmic solutions that directly drive automated business decisions and optimize operational efficiencies across enterprise systems.

What you will do

You will write clean, efficient, and modular Python code to build robust machine learning pipelines, preprocess complex datasets, and implement state-of-the-art deep learning architectures. Your daily tasks will involve hyperparameter tuning, evaluating model performance metrics using scikit-learn and TensorFlow, and deploying these predictive models into production environments using containerization tools while ensuring optimal latency and scalability.

Key responsibility

• Design, develop, and deploy production-ready machine learning models utilizing Python, TensorFlow, and scikit-learn to solve complex predictive modeling challenges.
• Execute rigorous exploratory data analysis on large-scale databases using advanced SQL queries, extracting meaningful statistical insights to guide feature engineering processes.
• Optimize existing data pipelines and feature stores to enhance model accuracy, reduce computational overhead, and ensure seamless integration with downstream applications.
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  Collaborate with cross-functional software engineering teams to translate theoretical machine learning prototypes into robust, scalable, and maintainable enterprise software solutions.
• Monitor deployed models continuously in production environments, identifying performance drift and executing scheduled retraining routines to maintain sustained prediction accuracy over time.
• Conduct comprehensive code reviews, write extensive unit tests, and maintain rigorous technical documentation for all developed algorithms and data transformation workflows.

Required Qualification and Skills

• Bachelor's or Master's degree in Computer Science, Statistics, Applied Mathematics, Data Science, or a closely related quantitative technical discipline.
• Demonstrated hands-on experience of 0 to 1 years in developing and deploying machine learning models in academic or professional project settings.
• Proficiency in programming languages such as Python and R, with a strong command over data structures, algorithms, and object-oriented programming paradigms.
• Deep working knowledge of machine learning frameworks and libraries including TensorFlow, PyTorch, scikit-learn, and Keras for neural network development.
• Strong expertise in database management systems, writing complex SQL queries, and extracting datasets from relational databases like PostgreSQL and MySQL.

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  Familiarity with statistical analysis software, data visualization tools like Tableau and Power BI, and basic understanding of big data technologies like Hadoop and Spark.

Benefits Included

• Comprehensive health insurance coverage extending to immediate family members, ensuring complete medical security and wellness support.
• Generous annual paid time off, casual leaves, and standard national holidays to maintain an optimal work-life balance.
• Regular professional development allowances, sponsored certifications, and access to advanced technical training platforms for continuous career growth.
• Performance-based annual bonuses, stock options, and periodic salary increments tied directly to individual and company milestones.
• Collaborative, inclusive, and modern office workspaces equipped with complimentary meals, recreational facilities, and ergonomic workstations.

A Day in the Life

Your day begins by reviewing real-time model performance dashboards to monitor prediction accuracy and detect any emerging data drift. You then spend the morning writing optimized Python code for feature extraction from massive SQL databases. After a collaborative sync-up with the data engineering team, you dedicate your afternoon to training deep learning models using TensorFlow, analyzing loss curves, and fine-tuning hyperparameters before wrapping up by documenting your experimental results.