We are seeking a talented and experienced Machine Learning Engineer/Data Scientist with a proven track record in developing and deploying machine learning models. The ideal candidate should have 3-6 years of experience in Python, R, or SQL, with expertise in data manipulation and visualization libraries such as scikit-learn or pandas.

Responsibilities:

1. Develop and deploy machine learning models using frameworks such as TensorFlow, PyTorch, Scikit-Learn, or Keras.
2. Utilize big data technologies and tools including Hadoop, Spark, etc. to handle large volumes of data efficiently.
3. Work with cloud platforms such as AWS and Azure to deploy and manage machine learning models.
4. Implement ML Ops practices including versioning, serving, performance tuning, and monitoring models in production environments.
5. Collaborate with cross-functional teams including data engineers, software developers, and business stakeholders to deliver end-to-end machine learning solutions.
6. Stay updated with the latest advancements in machine learning, big data technologies, and cloud platforms to continuously improve the development and deployment processes.

Requirements:

1. Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or related field.
2. 3-6 years of experience in Python, R, or SQL for data manipulation and analysis.
3. Strong understanding and practical experience in building and deploying machine learning models.
4. Proficiency in using machine learning frameworks such as TensorFlow, PyTorch, Scikit-Learn, or Keras.
5. Experience with big data technologies like Hadoop and Spark.
6. Familiarity with cloud platforms such as AWS and Azure for deploying and managing machine learning models.
7. Knowledge of ML Ops practices including versioning, serving, performance tuning, and monitoring models in production.
8. Excellent problem-solving skills and ability to work independently as well as in a team environment.
9. Strong communication skills and ability to effectively communicate technical concepts to non-technical stakeholders.