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Myticas Consulting

Machine Learning/mlops Python Developer (32124)

Company: Myticas Consulting

Location: Greater Chicago Area

Posted on: January 08

Job Title: Machine Learning Development/MLOps Python Developer

Myticas's direct client based out of North Chicago, IL, is currently seeking a Machine Learning Development/MLOps Python Developer for a contract position.


Required Skills, Qualifications, and Experience:

  • Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
  • 3-5 years of experience in developing and deploying machine learning models.
  • Proficiency in Python and experience with machine learning libraries such as TensorFlow, PyTorch, or Keras.
  • Strong understanding of computer vision techniques and tools.
  • Experience with MLOps practices, including CI/CD, automated model testing, and monitoring.
  • Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and containerization technologies like Docker and Kubernetes.
  • Excellent problem-solving skills and the ability to work independently as well as collaboratively in a fast-paced environment.
  • Effective communication skills to convey complex technical concepts to non-technical stakeholders.


Job Description Summary:

We are seeking a skilled and motivated Machine Learning Development/MLOps Python Developer to join our team. In this role, you will be instrumental in the development, training, and deployment of vision models into production environments. You will collaborate with a talented team to design and implement machine learning solutions that address complex problems and enhance our capabilities.


Key Responsibilities:

  • Design, develop, and train vision models to meet specific business objectives.
  • Deploy machine learning models to production environments efficiently and reliably.
  • Ensure scalability, robustness, and performance in the deployment of machine learning models.
  • Collaborate with cross-functional teams to understand requirements and translate them into data-driven solutions.
  • Integrate machine learning models with existing systems and develop APIs for model consumption.
  • Implement continuous integration/continuous deployment (CI/CD) pipelines to automate model training and deployment processes.
  • Monitor and optimize the performance of deployed models to ensure they meet expected benchmarks.
  • Document processes, results, and learnings to facilitate knowledge sharing and improve future iterations.

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