Note: The job is a remote job and is open to candidates in USA. CareerUS Solutions is seeking an experienced Machine Learning Engineer to design, develop, deploy, and optimize machine learning models and AI-driven solutions. The role focuses on transforming complex data into scalable, production-ready applications and monitoring and improving deployed models.
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
Skills
- 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
- 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
Company Overview