Junior PyTorch And Deep Learning Computer Vision Engineer

What role will you play in the team: As a Junior PyTorch and Deep Learning Computer Vision Engineer, you will play a pivotal role in designing, training, and deploying advanced neural network architectures. You will collaborate closely with senior data scientists and machine learning engineers to transform complex visual data into actionable business insights, building foundational models that power our enterprise-grade AI applications across multiple industry verticals.

What you will do: You will actively experiment with state-of-the-art computer vision algorithms, preprocess massive image and video datasets, and optimize model inference performance. Your day-to-day tasks will involve writing clean, modular Python code, leveraging scikit-learn for baseline metrics, and fine-tuning deep learning models using PyTorch to ensure high accuracy and low latency in production environments.

Key responsibility:

• Design, implement, and validate convolutional neural networks (CNNs) and transformer-based vision architectures using PyTorch and scikit-learn.
• Clean, augment, and manage large-scale unstructured image and video datasets to ensure optimal model training conditions.
• Perform rigorous statistical analysis and hyperparameter tuning to evaluate model performance, utilizing Statistical Analysis Software and Minitab for experimental validation.
• Integrate trained machine learning models with RESTful APIs and webhooks for seamless deployment into cloud-native production architectures.
• Collaborate with data engineering teams to build automated ETL pipelines using Talend and Apache Nifi for continuous data ingestion.
• Document experimental procedures, model architectures, and validation metrics thoroughly for cross-functional technical teams.
• Monitor deployed models for data drift and concept drift, proactively retraining algorithms to maintain high prediction accuracy over time.

Required Qualification and Skills:

• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or a related quantitative field.
• Solid foundational experience with Python programming, including NumPy, Pandas, and scikit-learn.
• Hands-on practical knowledge of building and training neural networks using PyTorch.
• Familiarity with SQL, PostgreSQL, and database management for querying and handling relational data sets.
• Understanding of statistical analysis software like SAS, SPSS, or Minitab for hypothesis testing and distribution validation.
• Strong problem-solving abilities, mathematical aptitude, and a keen eye for detail in machine learning workflows.

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  Effective communication skills to articulate complex algorithmic concepts to non-technical stakeholders.

Benefits Included:

• Comprehensive health insurance coverage for employee and immediate family members.
• Competitive annual performance-based bonuses and quarterly milestone rewards.
• Flexible working hours with modern ergonomic office setups and recreational zones.
• Dedicated annual learning and development budget for certifications and conference attendance.
• Regular team-building events, hackathons, and wellness programs designed to foster innovation and collaboration.

A Day in the Life: Your day begins with a quick stand-up meeting with the data science team to review model training logs from overnight experiments. You then spend the morning writing Python scripts to preprocess a new batch of image data and fine-tuning a PyTorch network architecture. After lunch, you analyze the validation results using scikit-learn metrics, debug API webhook integrations, and collaborate with the engineering lead on optimizing model inference latency before wrapping up your documentation.