MLOps Engineer

Quantum World Technologies Inc. • durham, nc, us • 1d ago

Job Summary:

Key Responsibilities:

ML Application Development: Develop, test, and maintain high-quality ML-based software applications using Python, Machine Learning libraries, Typescript, Web frameworks like React, and other relevant technologies.
Scalable Solutions: Design and implement scalable and efficient solutions on AWS, ensuring robust performance and security.
Data-Driven Decision Making: Support data-driven decision making by using Prefect for workflow orchestration and SQL, Snowflake for data warehousing.
Model Development: Develop and deploy machine learning models using Python and relevant libraries/frameworks.
Integration: Integrate machine learning solutions with existing data pipelines and DevOps practices.
Production Management: Manage production-level code and ensure the reliability of machine learning models in a live environment.
Containerization: Utilize Docker and Kubernetes for containerization and orchestration of applications.
Collaboration: Collaborate with cross-functional teams to capture requirements, design solutions, and ensure successful project delivery.
Support and Troubleshooting: Provide production support, troubleshoot issues, and implement fixes to ensure the smooth operation of software applications.
Code Reviews and Best Practices: Participate in code reviews, contribute to standard methodologies, and continuously improve the development process.
Industry Trends: Stay updated with the latest industry trends and technologies to ensure our solutions remain at the forefront of innovation.

Minimum Qualifications:

MLOPs and CI/CD
MLOps, renowned for designing and deploying cutting-edge ML models and orchestrating end-to-end pipelines
Implemented automated MLOps pipelines and model monitoring using Jenkins, Prometheus, and Grafana, reducing deployment time ensuring high reliability.
Led a team of data scientists and Data & ML engineers, mentoring junior members and aligning AI initiatives and MLOps executives team.
Developed Docker Compose stacks for MLOps services, MLFlow and workflow orchestration for game analytics.
Designed an on-prem MLOps platform for seamless training, deployment, and monitoring of ML models.
Constructed and optimized MLOps pipelines on GCP's Vertex AI to guarantee the deployment, monitoring, and performance optimization of robust models.
Utilizing Azure Databricks, Data Factory, and Logic App with MLflow, an MLOps Pipeline Monitoring System was developed to detect data and model drift and send email notifications to the user, advising them to retrain the model if it becomes obsolete over time.
Implemented the MLOps guide for projects, commencing with the collection of business requirements and concluding with the operationalization of the model.
Deployed a document recommendation engine to production to recommend context-based documents based on checklists
Deployed an AI redaction model to conceal PII entities using NLP transformer models with an accuracy
Build Docu360 platform AI microservices to extract document metadata with state of art NLP models
Data preparation, Activity monitors along raising red ﬂags on data intricacies Build recommendation ranking on scalable documents
Build multi-language Document Redaction along with a feedback loop

Preferred Qualifications:

Education: Bachelor's degree in Computer Science, Engineering, or a related field.
Additional Languages: Experience with other programming languages and frameworks.
CI/CD and DevOps: CI/CD pipelines and DevOps practices.
Data Engineering: Knowledge of data engineering practices and tools.
Agile Methodologies: Familiarity with Agile methodologies and project management tools.

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