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Job Title: Machine Learning Engineer
Job Description:
We are seeking a talented and experienced Machine Learning Engineer to join our dynamic team. In this role, you will be responsible for designing, developing, and deploying machine learning models and algorithms to solve complex business problems. You will work closely with data scientists and software engineers to build scalable and robust ML systems, from data preprocessing and feature engineering to model training, evaluation, and deployment.
Key Responsibilities:
* Develop, train, and evaluate machine learning models using various algorithms and techniques.
* Implement and optimize ML pipelines for data ingestion, preprocessing, and feature engineering.
* Deploy ML models into production environments, ensuring scalability and performance.
* Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions.
* Stay up-to-date with the latest advancements in machine learning and artificial intelligence.
* Conduct research and experimentation to identify and implement new ML methodologies.
* Monitor and maintain deployed ML models, identifying and resolving issues.
* Contribute to the development of MLOps best practices and tooling.
Qualifications:
* Bachelor’s or Master’s degree in Computer Science, Engineering, Statistics, or a related quantitative field.
* Proven experience in developing and deploying machine learning models.
* Strong programming skills in Python and relevant ML libraries (e.g., TensorFlow, PyTorch, scikit-learn).
* Experience with data manipulation and analysis tools (e.g., Pandas, NumPy).
* Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and their ML services.
* Solid understanding of ML algorithms, statistical modeling, and data mining techniques.
* Excellent problem-solving and analytical skills.
* Strong communication and collaboration abilities.
Preferred Qualifications:
* Experience with distributed computing frameworks (e.g., Spark).
* Knowledge of MLOps principles and tools (e.g., Docker, Kubernetes, MLflow).
* Experience with big data technologies.
* Contributions to open-source ML projects.