Deskripsi Pekerjaan
Are you a Data Scientist with a passion for building robust, scalable machine learning platforms? We are seeking a highly skilled MLOps Platform Engineer to join our high-performing Agile platform team. As part of our centralized ML & AI ART (Agile Release Train), you will play a critical role in bridging the gap between data science experimentation and production-grade deployment.
In this role, you will design, build, and maintain the infrastructure that powers our cutting-edge AI initiatives. You will work closely with data scientists to optimize model performance, streamline CI/CD pipelines for machine learning, and ensure that our platform remains reliable, secure, and performant at scale. If you are a problem-solver who enjoys working at the intersection of software engineering and data science, we want to hear from you.
Tanggung Jawab
- Design and maintain scalable end-to-end ML pipelines to accelerate model development and deployment.
- Collaborate with cross-functional teams to integrate ML models into production environments seamlessly.
- Implement MLOps best practices, including versioning, automated testing, and model monitoring.
- Develop and optimize infrastructure as code (IaC) for our machine learning platform.
- Troubleshoot performance bottlenecks in model training and inference pipelines.
- Provide technical guidance on containerization and orchestration (e.g., Kubernetes, Docker) to data science teams.
- Ensure data security, governance, and compliance standards are met across all ML workflows.
Kualifikasi
- Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related quantitative field.
- Proven experience as a Data Scientist, ML Engineer, or MLOps Engineer in an agile environment.
- Strong proficiency in Python and experience with ML frameworks such as PyTorch, TensorFlow, or Scikit-learn.
- Expertise in cloud platforms (AWS, GCP, or Azure) and container orchestration tools like Kubernetes.
- Experience with CI/CD tools (e.g., Jenkins, GitLab CI, GitHub Actions) and infrastructure automation.
- Solid understanding of distributed systems and big data technologies (e.g., Spark, Kafka).
- Excellent communication skills with the ability to translate complex technical concepts for stakeholders.