AI / Machine Learning Engineer

We are seeking an experienced AI / Machine Learning Engineer with 3 to 4 years of hands-on expertise in designing, training, evaluating, and deploying machine learning models into production environments. This role involves working extensively on computer vision, classical machine learning, and deep learning applications. The ideal candidate will manage the entire ML lifecycle, from handling datasets and building training pipelines to deploying inference services. This position is focused on engineering and model development rather than data analysis or prompt engineering.

Key Responsibilities

Model Development & Training
Design and train machine learning and deep learning models tailored for real-world problems. Work across diverse problem types including computer vision, tabular data (classification and regression), time-series forecasting, and anomaly detection. Select appropriate algorithms thoughtfully, avoiding default reliance on deep learning. Perform hyperparameter tuning and optimize models for better performance. Analyze model failures systematically and iterate improvements based on data-driven insights.

Data & Feature Engineering
Understand, clean, and preprocess both structured and unstructured datasets. Conduct feature engineering specifically for classical machine learning models. Address challenges such as missing data, outliers, class imbalance, and label noise. Develop robust train/validation/test splits to prevent data leakage and ensure reliable evaluation.

Computer Vision
Build and deploy computer vision models for tasks like image classification, object detection, and segmentation. Work with image preprocessing, augmentation, and labeling workflows. Fine-tune pre-trained vision models when appropriate to enhance performance.

Deployment & Production
Convert trained models into production-grade inference services, deploying them via APIs, batch pipelines, or streaming systems. Optimize inference for latency, throughput, and cost efficiency. Integrate models seamlessly with backend systems and product platforms. Monitor deployed models continuously and retrain them as necessary to maintain accuracy.

MLOps & Lifecycle Management
Develop and maintain end-to-end machine learning pipelines. Track experiments, datasets, and model versions systematically. Automate training, evaluation, and deployment processes. Implement rollback mechanisms and version control for models. Monitor model drift and changes in data distribution to ensure ongoing model reliability.

Required Qualifications

Core Machine Learning Knowledge
Strong foundation in machine learning algorithms, statistics, probability, and linear algebra. Clear understanding of when to apply linear models, tree-based models, and neural networks.

Tools & Frameworks
Proficiency in Python programming. Experience with machine learning frameworks such as scikit-learn, PyTorch, and/or TensorFlow. Familiarity with computer vision libraries like OpenCV and torchvision. Solid understanding of model evaluation metrics across various problem domains.

Deployment & Engineering Skills
Experience deploying machine learning models into production environments. Knowledge of Docker and containerized ML services. Familiarity with model serving frameworks and cloud or on-premises deployment setups. Ability to write maintainable, testable ML code beyond exploratory notebooks.

This role offers the opportunity to work on cutting-edge AI/ML projects with a focus on end-to-end engineering and production deployment. Candidates passionate about building scalable, reliable machine learning systems and advancing their expertise in both classical and deep learning methods will find this position rewarding.