Junior Machine Learning Engineer

What role will you play in the team

As a Junior Machine Learning Engineer at our organization, you will play a pivotal role in designing, developing, and deploying robust predictive models and deep learning architectures. You will collaborate closely with senior data scientists and software engineers to translate complex business requirements into scalable machine learning pipelines, ensuring high accuracy and optimal performance across all production environments in our tech ecosystem.

What you will do

You will actively participate in end-to-end machine learning lifecycle management. Your daily tasks will involve data collection, rigorous exploratory data analysis, feature engineering, and model training utilizing advanced frameworks. Furthermore, you will monitor model drift, optimize hyperparameters, containerize applications using Docker, and deploy microservices to cloud infrastructure, ensuring seamless integration with existing enterprise software applications.

Key responsibility

• Design, train, and evaluate supervised and unsupervised machine learning models using Python and scikit-learn.
• Implement deep learning architectures for computer vision and natural language processing tasks using TensorFlow and PyTorch.
• Write complex SQL queries and optimize database schemas in PostgreSQL and MySQL for high-throughput data retrieval.
• Build robust ETL pipelines using Apache Nifi and Talend to ingest massive streams of structured and unstructured data.
• Perform statistical analysis and validate hypotheses to ensure data integrity before feeding into training algorithms.
• Collaborate with cross-functional teams to integrate RESTful APIs powered by trained predictive models into web applications.
  
• Conduct rigorous code reviews, unit testing, and continuous integration/continuous deployment (CI/CD) practices for all model updates.
• Document technical specifications, data dictionaries, and model performance metrics for auditing and compliance purposes.

Required Qualification and Skills

• Bachelor's degree in Computer Science, Statistics, Mathematics, or a related quantitative discipline from a recognized institution.
• Demonstrated hands-on experience of 0 to 1 years in building and deploying machine learning models in academic or professional settings.
• Proficiency in Programming Languages including Python and R for data manipulation and statistical computations.
• Strong working knowledge of Machine Learning Frameworks such as TensorFlow, scikit-learn, Keras, and PyTorch.
• Hands-on expertise in Database Management systems including SQL, MongoDB, PostgreSQL, and MySQL.
• Familiarity with Big Data Technologies like Hadoop, Spark, and Apache Kafka for handling large-scale datasets.
• Familiarity with Data Visualization Tools such as Tableau, Power BI, and Google Data Studio for presenting model insights.
• Solid understanding of Statistical Analysis Software including SAS, SPSS, and Minitab for quantitative validation.

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• Excellent problem-solving skills, algorithmic thinking, and the ability to work collaboratively in a fast-paced agile team environment.

Benefits Included

• Competitive annual compensation package structured with performance-based bonuses and annual appraisals.
• Comprehensive health, medical, and life insurance coverage for employees and their immediate family members.
• Flexible working hours with robust remote and hybrid infrastructure support to maintain optimal work-life balance.
• Generous paid time off, maternity/paternity leave, and mental health wellness days off.
• Dedicated annual learning and development budget for obtaining professional certifications, attending conferences, and online courses.
• State-of-the-art office facilities equipped with ergonomic workspaces, complimentary snacks, and recreational zones.

A Day in the Life

Your day begins with a quick stand-up meeting with the engineering squad to discuss sprint goals and blockages. You spend the morning writing Python scripts to clean a newly acquired dataset and engineer relevant features for a churn prediction model. Post-lunch, you fine-tune hyperparameters in a PyTorch neural network to improve validation accuracy, followed by running evaluation scripts. The day concludes with committing your code to the repository and documenting experimental results in the team wiki.