Junior Machine Learning and TensorFlow Model Deployment Engineer

What role will you play in the teamYou will play a pivotal role in bridging the gap between raw data and production-ready machine learning solutions. As part of our dynamic data science engineering group, you will collaborate closely with senior data scientists and ML infrastructure specialists to build robust prediction pipelines, optimize neural networks using TensorFlow, and ensure seamless integration of predictive models into enterprise web applications operating at scale within our Jaipur development hub.What you will doYou will design, train, evaluate, and deploy scalable machine learning models and deep learning architectures. Your daily routine will involve writing modular Python code, preprocessing large-scale datasets, fine-tuning hyperparameters, and containerizing models for cloud deployment. Additionally, you will monitor model drift, conduct rigorous performance benchmarking, and troubleshoot latency bottlenecks to ensure high availability and predictive accuracy across multiple business units.Key responsibilityDevelop and train predictive classification and regression models utilizing Python, scikit-learn, and TensorFlow frameworks for complex business forecasting.Containerize trained machine learning models using Docker and orchestrate deployment pipelines for real-time inference endpoints.Perform comprehensive feature engineering, missing value imputation, and dimensionality reduction on high-dimensional relational database tables.Collaborate with data engineering teams to consume streaming data feeds via Apache Kafka for live scoring mechanisms.Write automated unit and integration tests for machine learning modules to maintain high code coverage and reliability standards.Optimize neural network architectures to reduce memory footprint and execution time on resource-constrained staging servers.Document model architectures, hyperparameter configurations, and deployment instructions thoroughly for cross-functional engineering teams.Required Qualification and SkillsBachelor's degree in Computer Science, Statistics, Mathematics, or a related quantitative discipline with foundational academic or project experience.Strong programming proficiency in Python, including object-oriented design principles and standard scientific computing libraries like NumPy and Pandas.Demonstrated hands-on project experience with machine learning frameworks specifically including TensorFlow, Keras, and scikit-learn.Solid understanding of statistical analysis, probability distributions, gradient descent optimization, and evaluation metrics such as ROC-AUC and F1-score.Familiarity with relational databases and writing complex SQL queries for data extraction and preprocessing tasks.Basic understanding of MLOps principles, version control systems like Git, and containerization tools like Docker.Excellent analytical problem-solving skills, attention to detail, and effective written and verbal communication abilities.Benefits IncludedCompetitive entry-level annual salary package commensurate with technical aptitude and problem-solving capability.Comprehensive health insurance coverage including hospitalization and outpatient benefits for self and immediate dependents.Structured mentorship program pairing junior engineers with seasoned principal data scientists for accelerated career growth.Flexible working hours with modern ergonomic workstation setups within our state-of-the-art Jaipur technology center.Annual learning and development stipend dedicated to obtaining recognized cloud and machine learning certifications.A Day in the LifeYour day begins with a quick stand-up meeting with the data science squad to review model validation metrics from the overnight training runs. You spend the morning writing Python scripts to clean a new customer churn dataset and experimenting with different TensorFlow neural network layers to improve classification accuracy. After lunch, you collaborate with the DevOps engineer to containerize a scikit-learn pipeline and configure API endpoints for staging tests. You wrap up your day by documenting your experimental results in the team wiki and reviewing pull requests from peers.