Machine Learning Engineer

Company: IT Services Organization

Key Skills: Python, Data Structures, GIT, Docker, CICD

Key Responsibilities:

End-to-End ML Development:

• Design, develop, test, deploy, maintain, and enhance machine learning solutions using best software engineering practices.

Cloud-Based ML Solutions:

• Implement scalable ML pipelines in cloud-based environments (AWS), utilizing technologies such as containerized applications, distributed processing frameworks, and CI/CD tools.
• Utilize orchestration tools like Airflow and Terraform for Infrastructure as Code (IaC).

Optimization and Performance:

• Optimize data science and machine learning models through code optimization and leveraging high-performance computing.

MLOps and Lifecycle Management:

• Focus on the end-to-end management of the model lifecycle using MLOps practices (e.g., versioning, deployment, monitoring).

Knowledge Sharing:

• Actively share data science knowledge within the organization and represent the company's data science expertise at conferences and workshops.

Required Expirence:

• 2+ years of experience in machine learning or data science.
• The ability to communicate fluently in English in a business environment, which is essential for cross-team collaboration and presenting technical information.
• Proficiency in Python and the associated Machine Learning ecosystem (e.g., libraries like TensorFlow, PyTorch, Scikit-learn, etc.).
• Solid understanding of data structures, algorithms, computability and complexity, computer architecture, software design principles, and testing strategies.
• Expertise in distributed computing within the cloud (particularly AWS), especially when dealing with large amounts of data and parallel computations.
• Strong grounding in math, statistics, and machine learning techniques like statistical tests, classification, predictive modeling, handling missing data, sampling, and weighting.
• Experience working with agile development practices, which would be helpful in a fast-paced, iterative development environment.

Other Desired Qualities:

• MLOps: Familiarity with managing the lifecycle of machine learning models through automation, versioning, and deployment pipelines.
• Cloud Expertise: A strong background in deploying ML solutions on cloud platforms (specifically AWS) is critical for this role.
• Performance Optimization: The ability to optimize models and systems for high performance and scalability.
• Collaboration: Being able to share and represent data science knowledge both internally and externally, showcasing expertise in the field.

Qualifications: Any degree in a field related to computer science, engineering, or statistics/mathematics.