Planet Pharma
From Planet Pharma's careers page
Status: listed on the employer's own careers page when we last checked (daily). Confirm on their site before applying.
Design and maintain scalable platforms supporting machine learning model development, testing, deployment, and monitoring.
Build automation frameworks that improve reliability, reproducibility, and operational efficiency.
Develop CI/CD processes for machine learning and production software environments.
Manage containerized workloads and distributed compute resources across cloud and hybrid infrastructure.
Create and optimize data ingestion, transformation, and processing pipelines for large-scale datasets.
Collaborate with engineers, data scientists, and research stakeholders to enable efficient experimentation and model development.
Implement observability, monitoring, and performance management solutions.
Automate infrastructure provisioning and configuration management using modern DevOps practices.
Support secure, scalable, and cost-effective AI/ML operations.
Contribute to platform architecture decisions and technical best practices.
Required Qualifications
Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field, or equivalent experience.
5+ years of experience in software engineering, platform engineering, DevOps, cloud engineering, or related disciplines.
Experience supporting machine learning, analytics, or data-driven technology environments.
Strong knowledge of containerization and orchestration technologies.
Experience developing CI/CD pipelines and infrastructure automation solutions.
Proficiency in Python and modern software development practices.
Familiarity with one or more major cloud platforms.
Knowledge of distributed computing concepts and large-scale processing environments.
Strong troubleshooting, performance optimization, and operational support skills.
Experience supporting research-oriented, scientific, healthcare, or advanced analytics environments.
Exposure to modern AI/ML frameworks and model lifecycle management.
Experience working with structured and unstructured data at scale.
Familiarity with workflow orchestration and infrastructure-as-code approaches.
Understanding of GPU-accelerated computing environments.
Experience supporting large-scale model training or advanced data processing workloads.
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