AI/ML Engineer

Requirements

About the Role:

AI/ML Engineer

Key Responsibilities:

Model Development & Deployment

• Design, build, and train machine learning models to support core product features.
• Experiment with supervised, unsupervised, and deep learning algorithms to solve business challenges.
• Deploy models into production environments and monitor their performance.
  

Data Handling & Feature Engineering

• Collect, clean, preprocess, and analyze large volumes of structured and unstructured data.
• Engineer features that enhance model performance and align with product requirements.
• Ensure data quality and consistency across the pipeline.
  

Model Optimization

• Evaluate model performance using relevant metrics (precision, recall, F1-score, ROC-AUC, etc.).
• Optimize models for speed and accuracy using hyperparameter tuning, ensemble methods, or transfer learning.
  

Collaboration & Integration

• Collaborate with backend and frontend teams to integrate AI/ML capabilities into products.
• Build APIs and services that expose ML models for consumption across applications.
• Work closely with data engineers and DevOps to deploy and scale ML pipelines.
  

Research & Innovation

• Stay updated on the latest trends, tools, and frameworks in AI/ML.
• Prototype and test innovative AI solutions, including NLP, computer vision, recommendation systems, or time series forecasting.
• Document methodologies, findings, and technical processes clearly.
  

Skills & Qualifications:

• Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or related field.
• 2+ years of hands-on experience in AI/ML model development and deployment.
• Strong knowledge of Python and ML libraries such as scikit-learn, TensorFlow, Keras, PyTorch, XGBoost, or similar.
• Experience with data manipulation tools like Pandas, NumPy, and visualization libraries such as Matplotlib or Seaborn.
• Understanding of ML lifecycle, including data preprocessing, model building, evaluation, and deployment.
• Familiarity with cloud platforms (AWS, GCP, Azure) and container technologies like Docker is a plus.
• Knowledge of REST APIs and integration of ML models with applications.
• Excellent problem-solving and analytical skills.
• Strong communication skills and ability to explain complex ML concepts to non-technical stakeholders.
  

Preferred (Nice to Have):

• Experience with Natural Language Processing (NLP), Computer Vision, or Reinforcement Learning.
• Exposure to MLOps, model monitoring, or CI/CD pipelines.
• Familiarity with data versioning tools like DVC, MLflow, or Kubeflow