MLOps Engineer

2 weeks ago

Navi Mumbai, Maharashtra, India LenDenClub Full time
About LenDenClub

LenDenClub is a leading peer-to-peer lending platform that provides an alternate investment opportunity to investors or lenders looking for high returns with creditworthy borrowers looking for short-term personal loans. With a total of 8 million users and 2 million+ investors on board, LenDenClub has become a go-to platform to earn returns in the range of 10%-12%. LenDenClub 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. LenDenClub provides a great way to diversify one's investment portfolio.

Why Work at LenDenClub?

LenDenClub is a certified great place to work. The certification comes from 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. As a LenDenite, you will be a part of an enthusiastic and passionate group of individuals who own and love what they do. At LenDenClub we believe in creating leaders and with you coming on board you get to work with complete freedom to chase your ultimate career goal without any inhibitions.

Job Description

We are seeking a highly skilled and experienced MLOps Engineer to join our team and play a critical role in bridging the gap between machine learning model development and production deployment. As a technology-forward company, we are dedicated to implementing cutting-edge machine learning solutions that drive our business forward. This role will play a crucial part in operationalizing artificial intelligence across our product lines, ensuring scalability, performance, and alignment with business goals.

Responsibilities:
  • Collaborate with data scientists and machine learning engineers to understand model requirements and design efficient MLOps pipelines.
  • Design, develop, and implement CI/CD pipelines for machine learning models, including version control, testing, and automated deployment (utilizing tools like GitLab CI/CD, Jenkins X).
  • Conduct continuous testing (CT) of models to ensure performance and identify potential issues before production deployment (using tools like Pachyderm, Metaflow).
  • Deploy machine learning models (NLP, computer vision, LLM) 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).
  • Design and implement API endpoints for model serving using frameworks like Flask, FastAPI, and manage API gateways such as Amazon API Gateway or Kong, ensuring optimal load balancing and fault tolerance.
  • Implement robust monitoring solutions using Prometheus, Grafana, or New Relic to track model performance, resource utilization, and operational health.
  • Utilize MLOps tools like MLflow or Kubeflow for model experimentation, tracking, and deployment.
  • Monitor model performance in production and implement strategies for model drift detection and mitigation (using tools like Evidently, SHAP).
  • 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.
  • Manage multiple models in production, including scaling resources, versioning, and rollbacks (using tools like Argo Workflows, Prefect).
  • Document all MLOps processes and model management activities to ensure transparency and accountability.
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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