MLOps Engineer
1 month ago
About the Role:
LenDenClub is a leading peer-to-peer lending platform that provides an innovative investment opportunity to investors or lenders seeking high returns with creditworthy borrowers looking for short-term personal loans. With a significant user base of 8 million and 2 million+ investors on board, LenDenClub has become a prominent platform to earn returns in the range of 10%-12%. The company offers investors a convenient medium to browse thousands of borrower profiles to achieve better returns than traditional asset classes. Moreover, LenDenClub is safeguarded by market volatility and inflation. It provides a great way to diversify one's investment portfolio.
Why LenDenClub?
LenDenClub is a certified great place to work, as certified by the Great Place to Work Institute, Inc. A globally renowned firm dedicated to evaluating companies for their employee satisfaction on the grounds of high trust and high-performance culture at workplaces.
Key Responsibilities:
As a highly skilled MLOps Engineer, you will play a critical role in bridging the gap between machine learning model development and production deployment. You will be responsible for designing, developing, and implementing MLOps pipelines for machine learning models. This will include version control, testing, and automated deployment using tools like GitLab CI/CD, Jenkins X. Additionally, you will conduct continuous testing (CT) of models to ensure performance and identify potential issues before production deployment using tools like Pachyderm, Metaflow. You will also deploy machine learning models to production on cloud platforms (AWS, GCP, Azure) ensuring scalability, security, and high availability using tools like AWS SageMaker, Google AI Platform, Azure Machine Learning Service. You will design and implement API endpoints for model serving using frameworks like Flask, FastAPI, and manage API gateways such as Amazon API Gateway or Kong. Furthermore, you will implement robust monitoring solutions using Prometheus, Grafana, or New Relic to track model performance, resource utilization, and operational health. You will utilize MLOps tools like MLflow or Kubeflow for model experimentation, tracking, and deployment. You will monitor model performance in production and implement strategies for model drift detection and mitigation using tools like Evidently, SHAP. You will also apply advanced practices in continuous testing, including regression, performance, and A/B testing to validate models post-deployment using tools like Selenium or PyTest for automation. Finally, you will manage multiple models in production, including scaling resources, versioning, and rollbacks using tools like Argo Workflows, Prefect.
Requirements:
Master's degree in Computer Science, Engineering, or a related field (or equivalent experience).
5+ years of experience in MLOps or a related field.
Proven experience in deploying Machine Learning (ML) and Deep Learning (DL) models to production.
Proficiency in programming languages and frameworks such as Python, R, Scala, TensorFlow, PyTorch, and Scikit-learn.
Experience in designing and implementing CI/CD pipelines for ML models with modern DevOps tools (e.g., Git, Jenkins, CircleCI) and containerization technologies (Docker, Kubernetes).
Hands-on experience of deploying NLP, computer vision, and LLM type of models.
Familiarity with cloud platforms like AWS, GCP, or Azure.
Proficiency in MLOps tools like MLflow or Kubeflow.
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