Junior Machine Learning Engineer - TensorFlow And Predictive Modeling

What role will you play in the team

You will play a pivotal foundational role within our core artificial intelligence engineering unit, focusing heavily on building, training, and deploying advanced machine learning models. Your primary objective will be translating complex business predictive problems into scalable, high-performance TensorFlow and scikit-learn architectures, driving data-informed decisions for our enterprise stakeholders across India.

What you will do

You will actively design, build, and optimize predictive modeling pipelines using Python and TensorFlow. Your daily routine will involve preprocessing large-scale datasets, conducting rigorous feature engineering, training deep neural networks, evaluating model accuracy metrics, and collaborating closely with data engineering teams to transition experimental prototypes into production-grade environments seamlessly.

Key responsibility

• Develop, train, and fine-tune complex machine learning algorithms using TensorFlow, scikit-learn, Keras, and PyTorch frameworks.
• Perform comprehensive data cleansing, exploratory data analysis, and advanced feature engineering on high-dimensional relational databases.
• Collaborate with software development squads to containerize machine learning models using Docker for robust cloud deployment.
• Monitor production model performance, tracking data drift, concept drift, and executing continuous retraining pipelines.
• Conduct hyperparameter tuning and optimization routines to achieve optimal latency, scalability, and predictive accuracy.
• Document model architectures, training procedures, and validation benchmarks to ensure complete compliance and reproducibility.

Required Qualification and Skills

• Possess a Bachelor's or Master's degree in Computer Science, Statistics, Applied Mathematics, or a closely related quantitative field.
• Demonstrated hands-on experience utilizing Python, TensorFlow, scikit-learn, Keras, and PyTorch for predictive modeling tasks.
• Strong foundational knowledge in statistical analysis software, regression techniques, classification algorithms, and clustering methodologies.
• Proficiency in writing optimized SQL queries for extracting, transforming, and loading structured datasets from relational database systems.
• Excellent collaborative communication skills with an analytical mindset and a structured approach to complex problem-solving.
• Understanding of version control tools such as Git and basic familiarity with continuous integration pipelines.

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Benefits Included

• Competitive base annual compensation package accompanied by performance-linked variable quarterly bonuses.
• Comprehensive health, medical, and life insurance coverage protecting employees and their immediate family dependents.
• Generous paid time off, casual leaves, national holidays, and structured parental leave programs.
• Continuous professional development allowance covering accredited machine learning certifications, online courses, and workshops.
• Dynamic, collaborative, and inclusive workplace environment featuring regular team-building events and sponsored tech conferences.

A Day in the Life

Your day begins by reviewing real-time model monitoring dashboards to assess the inference accuracy of deployed predictive services. You spend the morning writing optimized Python code to train a deep learning classifier using TensorFlow, followed by a collaborative sprint planning session with data architects. The afternoon involves tuning hyperparameters, debugging feature engineering scripts, and presenting experimental findings to your engineering lead.