
AI Engineer
1 day ago
As a machine learning expert, you will be responsible for developing and deploying robust AI models in a production environment. Your expertise in cloud-based platforms will be key to creating scalable solutions that deliver real business value.
About the Role- Design and implement end-to-end machine learning pipelines on cloud-based platforms.
- Collaborate with data scientists to translate models and algorithms into production-ready code.
- Manage the entire machine learning lifecycle, including data ingestion, model training, validation, and deployment.
- Implement best practices for machine learning operations to automate workflows and ensure model performance and reliability.
- Optimize ML models for performance, scalability, and cost efficiency on cloud platforms.
- Monitor and troubleshoot deployed models to ensure they meet performance and business requirements.
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
- 3+ years of experience in a machine learning or data science role.
- Hands-on experience with cloud services, specifically SageMaker.
- Proficiency in Python and standard ML libraries (e.g., scikit-learn, TensorFlow, PyTorch).
- Experience with containerization technologies like Docker.
- Solid understanding of MLOps principles and tools for continuous integration and delivery.
- Knowledge of data engineering principles and experience with big data technologies (e.g., Spark, Hadoop).
- Strong problem-solving skills and ability to work in a collaborative environment.
- Experience with other cloud services such as S3, Lambda, Glue, and Redshift.
- Certifications such as AWS Certified Machine Learning - Specialty.
- Experience with model serving frameworks (e.g., Flask, FastAPI).
- Experience with version control systems like Git.
- Familiarity with agile development methodologies.
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