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10Pearls is seeking an MLOps Engineer – ML Platform & Feature Store to build, operate, and scale core components of our machine learning platform. This role is ideal for a hands-on engineer who thrives in production ML environments, working closely with data scientists to enable reliable model training, evaluation, and deployment workflows. You will be responsible for managing feature pipelines, training jobs, MLflow operations, and evaluation systems, while ensuring platform stability, scalability, and reproducibility. This is a highly collaborative role working alongside Data Scientists and MLOps leadership to evolve the ML platform.
Job Responsibility:
Build and maintain feature pipelines using Feast, including feature definitions and materialisation jobs (batch + streaming)
Develop and manage training pipelines, including containerization, scheduling, dataset access, and artifact handling
Operate and maintain MLflow tracking server, managing experiments, models, and artifact storage
Execute model evaluation workflows, run evaluation suites, and support model promotion decisions
Enable data scientists by resolving issues related to environment setup, data access, compute, and reproducibility
Manage GPU-based workloads and ensure efficient scheduling and utilization
Support distributed data processing using Spark or similar frameworks
Ensure air-gap readiness by managing dependencies, pre-building images, and enabling offline deployments
Collaborate with MLOps Lead on platform improvements, scalability, and long-term architecture
Requirements:
Bachelor's degree in Computer Science, Engineering, or a related field (preferred)
3–5 years of experience in ML engineering, data engineering, or MLOps roles
Strong Python skills with experience in pandas, numpy, pyarrow, scikit-learn
Hands-on experience with feature stores (Feast preferred) or similar feature pipeline systems
Experience with MLflow or similar experiment tracking/model registry tools
Familiarity with distributed computing frameworks (Spark or equivalent)
Working knowledge of Docker, Kubernetes (kubectl, Helm), and containerized workflows
Experience handling GPU-based workloads
Strong problem-solving skills and ability to support cross-functional teams