AI / Machine Learning Engineer

AI / Machine Learning Engineer

We are looking for a pure AI / ML Engineer with 3 to 4 years of experience who can design, train, evaluate, and deploy machine learning models into production systems. This role covers computer vision, classical machine learning, and deep learning use cases.

This is not a data analyst role and not a prompt-engineering role. You are expected to work with datasets, models, training pipelines, and production inference end to end.

• Design and train machine learning and deep learning models for real-world use cases
• Work across problem types:
• Computer vision
• Tabular data (classification, regression)
• Time-series forecasting
• Anomaly detection
• Select appropriate algorithms instead of defaulting to deep learning
• Perform hyperparameter tuning and model optimization
• Analyze model failures and iterate based on data, not guesswork

• Understand, clean, and preprocess structured and unstructured datasets
• Perform feature engineering for classical ML models
• Handle:
• Missing data
• Outliers
• Class imbalance
• Label noise
• 
  
• Design proper train/validation/test strategies
• Prevent data leakage and evaluation mistakes

• Build and deploy CV models for tasks such as:
• Image classification
• Object detection
• Segmentation
• 
  
• Work with image preprocessing, augmentation, and labeling workflows
• Fine-tune pre-trained vision models when appropriate

• Convert trained models into production-grade inference services
• Deploy models via APIs, batch pipelines, or streaming systems
• Optimize inference for:
• Latency
• Throughput
• Cost
• Integrate models with backend systems and products
• Monitor model performance and retrain when needed

• Build and maintain end-to-end ML pipelines
• Track experiments, datasets, and model versions
• Automate training, evaluation, and deployment
• Implement rollback and version control for models
• Monitor model drift and data distribution changes

• Strong foundation in:
• Machine learning algorithms
• Statistics & probability
• Linear algebra
• 
  
• Clear understanding of when to use:
• 
  
• Linear models
• Tree-based models
• Neural networks

• Strong experience with Python
• Experience with ML frameworks:
• scikit-learn
• PyTorch and/or TensorFlow
• Experience with computer vision tools (OpenCV, torchvision, etc.)
• Familiarity with model evaluation metrics across different problem types

• Experience deploying ML models into production
• Familiarity with:
• Docker and containerized ML services
• Model serving frameworks
• Cloud or on-prem deployment environments
• Ability to write maintainable, testable ML code (not just notebooks)