Machine Learning Engineer - GBIT (Global Bridge InfoTech Inc) : Job Details

Job Description:

• We are seeking a skilled and motivated Machine Learning 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 solve 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, ensuring they meet expected benchmarks.
• Document the process, results, and learnings to facilitate knowledge sharing and improve future iterations.

Qualifications:

• 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 and collaboratively in a fast-paced environment.
• Effective communication skills to convey complex technical concepts to non-technical stakeholders.