Artificial Intelligence Engineer

Job Title: AI/ML Engineer

Experience: 4+ Years

Location: Remote

Job Type: Full-Time

Job Summary:

We are looking for a passionate and results-driven AI/ML Engineer with 4 years of experience in designing, building, and deploying machine learning models and intelligent systems. The ideal candidate should have solid programming skills, a strong grasp of data preprocessing, model evaluation, and MLOps practices.

You will collaborate with cross-functional teams including data scientists, software engineers, and product managers to integrate intelligent features into applications and systems.

Key Responsibilities:

• Design, develop, train, and optimize machine learning and deep learning models for real-world applications.
• Preprocess, clean, and transform structured and unstructured data for model training and evaluation.
• Implement, test, and deploy models using APIs or microservices (Flask, FastAPI, etc.) in production environments.
• Use ML libraries and frameworks like Scikit-learn, TensorFlow, PyTorch, Hugging Face, XGBoost, etc.
• Monitor and retrain models as needed for performance, accuracy, and drift mitigation.
• Collaborate with software and data engineering teams to operationalize ML solutions using MLOps tools.
• Stay updated with emerging trends in AI/ML and suggest enhancements to existing systems.

Required Skills and Qualifications:

• Bachelor’s or Master’s in Computer Science, Engineering, AI/ML, Data Science, or related field.
• 4+ years of hands-on experience in machine learning model development and deployment.
• Strong experience in Python and libraries like Pandas, NumPy, Scikit-learn, Matplotlib/Seaborn.
• Experience with deep learning frameworks such as TensorFlow, PyTorch, or Keras.
• Proficiency in model deployment using Flask, FastAPI, Docker, and REST APIs.
• Experience with version control (Git), model versioning, and experiment tracking (MLflow, Weights & Biases).
• Familiarity with cloud platforms like AWS (SageMaker), Azure ML, or GCP AI Platform.
• Knowledge of databases (SQL/NoSQL) and data pipelines (Airflow, Spark, etc.).
• Strong problem-solving and debugging skills, with an analytical mindset.