Junior PyTorch And Scikit-learn Computer Vision Engineer

What role will you play in the team

As a Junior PyTorch and scikit-learn Computer Vision Engineer at our Kochi development center, you will be an integral part of our core Data Science, Analytics & Machine Learning division. Your primary objective will be to design, build, and deploy robust image processing pipelines and deep learning models that extract actionable visual insights from complex datasets. You will collaborate closely with senior data scientists and software architects to transform raw image and video feeds into structured, machine-readable formats. Your core contribution will directly enhance our automated quality control and visual recognition systems, driving operational efficiencies for our enterprise clients across the globe.

What you will do

You will spend your days developing, training, and fine-tuning convolutional neural networks and classical machine learning algorithms using PyTorch, scikit-learn, and OpenCV. Your routine will involve writing clean, modular Python code, conducting rigorous hyperparameter tuning, evaluating model performance metrics, and optimizing inference speeds for production environments. Furthermore, you will actively participate in data cleaning, exploratory data analysis, and feature engineering tasks, ensuring that all training sets are meticulously curated, labeled, and validated prior to model ingestion.

Key responsibility

• Design and implement custom computer vision architectures using PyTorch and scikit-learn for automated visual classification and object detection tasks.
• Perform comprehensive data preprocessing, augmentation, and exploratory data analysis on large-scale image and video datasets.
• Optimize trained neural network models for low-latency inference on edge devices and cloud-based GPU instances.
• Collaborate with data engineering teams to establish scalable ETL pipelines for continuous image data ingestion and storage in PostgreSQL databases.
• Conduct thorough model evaluation, cross-validation, and error analysis to minimize false positives and enhance overall prediction accuracy.
• Document model training procedures, experiment tracking logs, and architecture specifications using modern MLOps frameworks.
• Participate in code reviews, architectural discussions, and agile sprint planning sessions with cross-functional technical stakeholders.

Required Qualification and Skills

• Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Statistics, or a related quantitative discipline.
• 0-1 years of hands-on professional or academic experience building and deploying machine learning models using Python.
• Demonstrated proficiency in deep learning frameworks including PyTorch, Keras, and scikit-learn.
• Strong foundational knowledge of statistical analysis software, linear algebra, probability, and optimization algorithms.
• Practical experience with database management systems, specifically SQL, PostgreSQL, and handling unstructured data storage.
• Familiarity with data visualization tools such as Tableau or Power BI for presenting model performance dashboards.
• Excellent problem-solving abilities, strong verbal and written communication skills, and the capacity to thrive in a fast-paced team environment.

Benefits Included

• Competitive entry-level annual compensation package with performance-based quarterly appraisals and retention bonuses.
• Comprehensive health insurance coverage extending to immediate family members and dependents.
• Sponsored access to top-tier machine learning certifications, advanced cloud computing courses, and industry-leading technical workshops.
• Generous paid time off policy including casual leaves, sick leaves, and public holidays.
• State-of-the-art ergonomic workstations equipped with high-performance multi-GPU computing setups.
• Regular team-building activities, catered lunches, and wellness programs designed to promote a healthy work-life balance.

A Day in the Life

Your morning kicks off with a quick agile stand-up meeting to review yesterday's model training runs and set goals for the current sprint. Afterward, you dive into writing custom data augmentation scripts in Python to expand your training corpus. Post-lunch, you spend a few hours debugging a PyTorch loss function anomaly and tuning hyperparameters to boost your model's intersection-over-union score. The afternoon concludes with a code review session with your tech lead and updating the experiment tracking logs before logging off for the day.