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Company Overview
This company empowers IP and R&D teams by providing better answers, so they can make faster decisions with more confidence. Founded in 2007, the organization is a global leader in AI-powered IP and R&D intelligence. Their domain-specific LLM, trained on an extensive proprietary innovation data, coupled with their AI assistant, delivers actionable insights that increase productivity for IP tasks by 75% and reduce R&D wastage by 25%. IP and R&D teams collaborate better with a user-friendly platform across the entire innovation lifecycle. Over 15,000 companies trust this company to innovate faster with AI, including NASA, Tesla, PayPal, Sanofi, Dow Chemical, and Wilson Sonsini.
MLOps Engineer
About the Role
We are seeking a passionate MLOps Engineer to join our team and drive the deployment, monitoring, and optimization of machine learning models in production. This role will be key in ensuring the reliability, scalability, and efficiency of our ML infrastructure while supporting the development and release of AI-driven solutions. If you have a strong background in cloud technologies, automation, and ML model deployment, this is an excellent opportunity to work on cutting-edge AI applications.
Responsibilities
Design, build, and maintain scalable ML model deployment pipelines for real-time and batch inference.
Manage and optimize cloud-based ML infrastructure, ensuring high availability and cost efficiency.
Implement monitoring, logging, and alerting systems for ML models in production to track performance, data drift, and anomalies.
Automate model training, evaluation, and deployment processes using CI/CD pipelines.
Ensure compliance with MLOps best practices, including model versioning, reproducibility, and governance.
Collaborate with data scientists, ML engineers, and software developers to streamline the transition of models from development to production.
Optimize model serving infrastructure using Kubernetes, Docker, and serverless technologies.
Improve data pipelines for feature engineering, data preprocessing, and real-time data streaming.
Research and implement tools for scalable AI development, such as Retrieval-Augmented Generation (RAG) and agent-based applications.
Qualifications
Hands-on experience with MLOps platforms (e.g., MLflow, Kubeflow, TFX, SageMaker).
Strong expertise in cloud services (AWS, GCP, Azure and other Clouds).
Proficiency in containerization (Docker, Kubernetes) and infrastructure as code (Terraform, CloudFormation).
Experience in building CI/CD pipelines for machine learning models.
Solid programming skills in Python, Go, or Shell scripting for automation.
Familiarity with data versioning and model monitoring tools (DVC, Evidently AI, Prometheus, Grafana).
Understanding of feature stores and efficient data management for ML workflows.
Strong problem-solving skills with a proactive, self-motivated attitude.
Excellent collaboration and communication skills to work in a cross-functional team.
Fluent in Mandarin for effective communication within a multilingual team environment.
Disclaimer
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No Client Relationship: This company is not a client of SpringCube unless stated.
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