ML Engineer (Only W2)

Infinite Computer Solutions - Dallas, TX

Full-time

Dallas, TX

Professional, Scientific, and Technical Services

About the position

The ML Engineer role focuses on developing, deploying, and optimizing machine learning models in real-time environments. This position requires collaboration with data scientists and cross-functional teams to ensure seamless integration of machine learning solutions into existing infrastructures, while also monitoring performance and implementing improvements.

Responsibilities

• Collaborate with data scientists to develop, train, and validate machine learning models.
• Implement algorithms and techniques suitable for real-time data processing and inference.
• Design and implement robust deployment pipelines for machine learning models in real-time environments.
• Utilize cloud services (Google Cloud Platform or AWS) for deploying and scaling machine learning models.
• Architect and optimize end-to-end machine learning solutions that integrate seamlessly with existing infrastructure.
• Ensure solutions are built for scalability, maintainability, and high availability.
• Monitor model performance and ensure real-time systems are operating at optimal levels.
• Implement logging, tracking, and alerting mechanisms to identify and address model drift or system failures.
• Work closely with cross-functional teams, including data engineers, software developers, and product managers, to align on project goals and deliverables.
• Communicate technical concepts to non-technical stakeholders effectively.
• Create and maintain documentation for model development, deployment processes, and system architecture.
• Document best practices and contribute to knowledge-sharing initiatives within the team.
• Stay up-to-date with the latest trends in machine learning and cloud technologies.
• Proactively identify areas for improvement in existing processes and models.

Requirements

• Bachelor's degree in Computer Science, Data Science, Mathematics, or a related field; Master's degree preferred.
• 3+ years of experience in machine learning engineering, data science, or related fields.
• Proven experience with real-time model deployment on cloud platforms (AWS, Google Cloud Platform).
• Familiarity with tools like TensorFlow, PyTorch, Scikit-learn, or similar libraries.
• Proficient in programming languages such as Python, Java, or Scala.
• Strong understanding of data structures, algorithms, and machine learning concepts.
• Experience with containerization technologies (Docker, Kubernetes) for model deployment.
• Knowledge of cloud services like AWS SageMaker, Google AI Platform, or similar.

Nice-to-haves

• Experience with big data technologies (e.g., Apache Spark, Hadoop).
• Familiarity with monitoring and observability tools (e.g., Prometheus, Grafana).
• Understanding of CI/CD pipelines for machine learning (e.g., MLflow, Kubeflow).