Machine Learning Engineer

Job Summary

We are looking for an experienced Machine Learning Engineer to design, develop, deploy, and optimize machine learning models and AI-driven solutions. The ideal candidate will have strong programming skills, experience with machine learning algorithms, and the ability to transform complex data into scalable, production-ready applications.

Key Responsibilities

• Design, develop, train, and deploy machine learning models for real-world business problems.
• Collect, clean, preprocess, and analyze large datasets for model development.
• Develop predictive models using supervised and unsupervised learning techniques.
• Apply statistical methods and machine learning algorithms to solve business challenges.
• Build and optimize data pipelines for model training and inference.
• Evaluate model performance using appropriate metrics and validation techniques.
• Collaborate with data scientists, data engineers, software engineers, and product teams.
• Deploy machine learning models into production environments.
• Monitor model performance, detect data drift, and improve model accuracy.
• Implement model versioning, reproducibility, and automated ML workflows.
• Optimize models for scalability, performance, and computational efficiency.
• Maintain technical documentation and follow best practices for model development.

Required Skills and Qualifications

• Bachelor's or master's degree in Computer Science, Data Science, Statistics, Engineering, or a related field.
• Strong programming skills in Python.
• Experience with machine learning libraries such as Scikit-learn, TensorFlow, or PyTorch.
• Knowledge of supervised learning, unsupervised learning, classification, regression, and clustering.
• Strong understanding of statistics, probability, and model evaluation techniques.
• Experience with data preprocessing, feature engineering, and model optimization.
• Familiarity with SQL and data processing techniques.
• Experience deploying models using APIs, containers, or cloud platforms.
• Strong analytical, problem-solving, and communication skills.

Preferred Skills

• Experience with AWS, Microsoft Azure, or Google Cloud Platform.
• Knowledge of MLOps tools and practices for model deployment and monitoring.
• Experience with Docker, Kubernetes, Git, and CI/CD pipelines.
• Familiarity with large language models (LLMs), natural language processing (NLP), or computer vision.
• Experience with MLflow, Kubeflow, or similar machine learning lifecycle tools.
• Knowledge of distributed computing frameworks such as Apache Spark.