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 play a crucial foundational role in designing, building, and deploying scalable predictive models and deep learning architectures. You will collaborate closely with senior data scientists and MLOps engineers to translate complex business requirements into high-performing algorithmic solutions that drive measurable operational impact across our enterprise platforms.

What you will do

You will actively participate in the complete lifecycle of machine learning model development, from exploratory data analysis and rigorous feature engineering to model training, hyperparameter tuning, and production deployment. Your daily tasks will involve writing clean, modular Python code, optimizing database queries using SQL, managing large datasets with Apache Spark, and continuously monitoring model drift to ensure sustained prediction accuracy in live environments.

Key responsibility

• Design, develop, and implement robust machine learning algorithms and deep learning pipelines using Python, TensorFlow, and scikit-learn.
• Perform extensive exploratory data analysis, data cleaning, and advanced feature engineering on large-scale structured and unstructured datasets.
• Optimize SQL queries and manage data extraction processes from relational databases like PostgreSQL and MySQL for model ingestion.
• Collaborate with data engineering teams to integrate ETL pipelines using Apache Nifi and Talend for seamless data flow.
• Containerize machine learning models and deploy them onto cloud infrastructure using Docker and Kubernetes for scalable inference.
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  Conduct rigorous model evaluation, validation, and cross-validation to minimize overfitting and maximize predictive performance metrics.
• Monitor production model performance, latency, and resource utilization, implementing automated retraining strategies when performance degrades.
• Document technical specifications, model architectures, and experimental results comprehensively for cross-functional engineering teams.

Required Qualification and Skills

• Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Data Science, or a closely related quantitative technical field.
• Strong foundational proficiency in programming languages, specifically Python, with deep working knowledge of object-oriented programming principles.
• Hands-on experience with core machine learning frameworks including TensorFlow, scikit-learn, Keras, and PyTorch.
• Solid understanding of database management systems and querying languages such as SQL, PostgreSQL, and MySQL.
• Familiarity with big data technologies like Apache Spark and Hadoop for processing distributed datasets efficiently.
• Working knowledge of data visualization tools such as Tableau and Power BI to present analytical insights clearly.
• Excellent analytical, problem-solving, and mathematical skills with a strong grasp of linear algebra, calculus, and statistical probability.

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  Ability to work effectively in a fast-paced, collaborative team environment with strong verbal and written communication skills.

Benefits Included

• Comprehensive health insurance coverage including medical, dental, and vision plans for employees and their immediate dependents.
• Competitive annual base salary with performance-based bonuses and annual appraisal reviews based on technical deliverables.
• Flexible working hours and hybrid work arrangement policies to ensure a healthy work-life integration.
• Generous paid time off, casual leaves, sick leaves, and parental leave policies for new parents.
• Dedicated annual learning and development stipend for attending professional conferences, certifications, and online courses.
• Well-stocked office pantry, complimentary daily meals, snacks, and beverages for days working from the office.
• Regular team-building events, recreational activities, and annual company retreats to foster a collaborative culture.

A Day in the Life

Your typical day begins with a quick stand-up meeting with your squad to discuss sprint progress and roadblocks. You will then spend your morning writing Python scripts to train a new classification model using scikit-learn and evaluating its accuracy metrics. After lunch, you will collaborate with the data engineering team to optimize SQL queries for faster data retrieval and review code deployment logs for your productionized models.