Company Overview
10Pearls is an award-winning end-to-end digital innovation company that helps businesses imagine and build the future. We are proud to announce that 10Pearls was named as winner of the Best Tech Work Culture Timmy Award in Washington DC by Tech in Motion, recognized on the Inc. 5000 Fastest-Growing Companies List, and was ranked the #1 Most Diverse Midsize Company in Greater Washington. We partner with businesses to help them transform, scale, and accelerate by adopting digital and exponential technologies. Our work has ranged from creating highly usable, secure digital experiences, mobile and software products, to helping businesses modernize through cloud adoption and development and the digitalization of their business processes. Our clientele is highly diverse, including Global 1000 enterprises, mid-market businesses, and high-growth start-ups. But those are just the facts. What makes us unique is that we have true heart and soul. We have a strong focus on a double bottom line and actively support and engage with the communities where we live and work to make the world a better place. In a nutshell, we believe in doing well, while doing good, and know how to balance the two.
Role
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.
Responsibilities
• 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
Nice to Have
• Experience scaling Feast materialisation pipelines
• Familiarity with Kubeflow, Tekton, or pipeline orchestration tools
• Exposure to LLM workflows (vLLM, TGI, fine-tuning)
• Experience with data versioning tools (DVC, LakeFS, Nessie)
• Familiarity with evaluation frameworks (RAGAS, DeepEval)
