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 architects to design, train, and deploy predictive models. Your core objective is to bridge the gap between experimental data science notebooks and high-performance production systems, ensuring seamless integration of complex machine learning algorithms into our enterprise applications.

What you will do

You will actively write clean, modular Python code to preprocess massive structured and unstructured datasets, engineer robust predictive features, and train advanced machine learning models using scikit-learn and TensorFlow. Additionally, you will monitor model drift, optimize inference latency, and containerize algorithmic pipelines to support scalable cloud deployments across various business units.

Key responsibility

• Design, implement, and validate robust machine learning models utilizing Python, scikit-learn, TensorFlow, and PyTorch for various enterprise predictive tasks.
• Extract, clean, and transform large-scale datasets from disparate sources using advanced SQL queries and Apache Spark big data pipelines.
• Collaborate with data engineering teams to establish automated ETL workflows, integrating Apache Kafka and Talend for real-time data streaming.
• Perform comprehensive hyperparameter tuning and cross-validation to maximize algorithmic accuracy and minimize operational latency.
• Deploy trained models into production environments using containerization tools, ensuring high availability and seamless API integration.
  
• Conduct thorough statistical analysis and exploratory data visualization utilizing Tableau and Power BI to communicate model insights to stakeholders.
• Document technical specifications, model architecture, and version control procedures rigorously within git repositories.

Required Qualification and Skills

• Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, or a highly related quantitative engineering discipline.
• Strong foundational programming proficiency in Python alongside deep working knowledge of R for statistical computations.
• Hands-on professional experience with core machine learning frameworks including scikit-learn, TensorFlow, Keras, and PyTorch.
• Expertise in database management systems, writing complex SQL queries, and working with MongoDB and PostgreSQL databases.
• Familiarity with big data technologies such as Apache Hadoop, Spark, and real-time streaming architectures via Apache Kafka.
• Demonstrated ability to utilize ETL tools like Informatica, Talend, and Apache Nifi for comprehensive data pipeline orchestration.
• Working knowledge of data visualization platforms including Tableau, Power BI, and Google Data Studio for analytical reporting.

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• Solid understanding of statistical analysis software packages such as SAS, SPSS, and Minitab for hypothesis testing.

Benefits Included

• Comprehensive health insurance coverage for employees and immediate family members, including OPD and hospitalization benefits.
• Annual performance-based bonuses tied directly to successful model deployments and operational efficiency metrics.
• Generous paid time off policy encompassing casual leaves, sick leaves, and earned annual vacations.
• Dedicated annual learning and development budget for attending international AI conferences, acquiring cloud certifications, and purchasing technical literature.
• Flexible working hours with ergonomic office infrastructure located in major technology hubs across India.
• Regular team-building events, subsidized gourmet cafeteria meals, and comprehensive wellness programs.

A Day in the Life

Your typical workday begins with a stand-up sync with the data science team to review model training metrics from overnight cloud jobs. You will spend the morning writing Python scripts to clean a new telemetry dataset and tuning hyperparameters in a TensorFlow neural network. After lunch, you will debug a data pipeline failure in Apache Spark, review a peer's pull request on GitHub, and present your weekly accuracy benchmarks to the engineering director.