Job description
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 a Senior Software Consultant – MLOps & Training Engineer to build and manage scalable machine learning pipelines, from data preprocessing and model training to deployment and monitoring. The ideal candidate will play a key role in operationalizing ML models, ensuring reliability, scalability, and performance across the ML lifecycle while collaborating closely with Data Scientists and Engineering teams.
Responsibilities
• Design, build, and maintain ML training pipelines for scalable and efficient model development.
• Develop robust data preprocessing and feature engineering workflows.
• Deploy machine learning models into production environments and ensure smooth integration.
• Implement model monitoring, logging, and performance tracking systems.
• Manage and optimize CI/CD pipelines for ML workflows.
• Work with containerization and orchestration tools (Docker, Kubernetes) for scalable deployments.
• Collaborate with Data Science and Engineering teams to productionize ML solutions.
• Ensure system reliability, scalability, and performance across ML pipelines.
• Troubleshoot, debug, and optimize ML systems and infrastructure.
Requirements
• 3–5 years of experience in MLOps, Machine Learning Engineering, or related roles.
• Strong experience with ML model training pipelines and deployment workflows.
• Experience with ML workflow orchestration tools (Airflow, Kubeflow, MLflow).
• Hands-on experience with data preprocessing and feature engineering.
• Familiarity with cloud ML platforms (Azure (must-have), GCP, or AWS).
• Experience with Docker, Kubernetes, and CI/CD pipelines.
• Strong programming skills (preferably in Python).
• Understanding of model monitoring, logging, and performance optimization.
• Strong problem-solving and analytical skills.
• Ability to work collaboratively in cross-functional teams.
Nice to Have
• Exposure to real-time or large-scale ML systems.
• Familiarity with data engineering pipelines and big data tools.
• Experience working in AI/ML-driven product environments.
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