What You’ll Do :
- Design and implement automated training and inference pipelines including building a
model registry system to track model artifacts, versioning, and lineage
Develop frameworks for on-demand model training and architect parallel processingsystems for inference and event-driven architectures with multi-optimization support
Design and develop sophisticated APIs that expose ML model capabilitiesBuild ETL pipelines specifically tailored for ML applications and develop preprocessingframeworks
Implement comprehensive monitoring solutions to identify system bottlenecks,optimize for enhanced scalability
Create infrastructure for offline and online experimentationBuild internal frameworks and tools that standardize ML workflows across theorganization
Stay current with developments in machine learning engineering and MLOps, evaluatingand recommending new technologies and best practices
Contribute to technical decision-making and architecture discussions to shape thefuture of our ML infrastructure.
Required Qualifications :
Bachelor's degree in Computer Science, Engineering, or related technical field (orequivalent experience)
3+ years of experience in software engineering with a focus on ML systemsStrong programming skills in Python and experience with ML frameworks (TensorFlow,PyTorch, Scikit-learn)
Experience building and maintaining production ML pipelinesProficiency with containerization technologies (Docker, Kubernetes)Experience with cloud platforms (AWS, GCP, or Azure) and their ML servicesUnderstanding of software engineering best practices including version control, CI / CD,and testing
Experience with data processing frameworksStrong problem-solving skills and ability to work independently on complex technicalchallenges
Excellent communication skills to collaborate with cross-functional teams