Job description
Key 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.Real-Time Deployment :Design and implement robust deployment pipelines for machine learning models in real-time environments.
Utilize cloud services (GCP or AWS) for deploying and scaling machine learning models.System Architecture :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.Performance Monitoring :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.Collaboration :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.Documentation :Create and maintain documentation for model development, deployment processes, and system architecture.
Document best practices and contribute to knowledge-sharing initiatives within the team.Continuous Improvement :Stay up-to-date with the latest trends in machine learning and cloud technologies.
Proactively identify areas for improvement in existing processes and models.Qualifications :
Education :Bachelor's degree in Computer Science, Data Science, Mathematics, or a related field. Master's degree preferred.
Experience :3 years of experience in machine learning engineering, data science, or related fields.
Proven experience with real-time model deployment on cloud platforms (AWS, GCP).Familiarity with tools like TensorFlow, PyTorch, Scikit-learn, or similar libraries.Technical Skills :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.Soft Skills :Strong analytical and problem-solving skills.
Excellent communication skills, with the ability to articulate complex ideas to diverse audiences.Ability to work in a fast-paced, agile environment and manage multiple priorities.Preferred Qualifications :
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).
