Job Summary:We are seeking a Senior AI & ML MLOps Engineer with 4 to 8 years of experience to join our team. The ideal candidate will have a strong background in MLOps practices, cloud infrastructure, automation, and machine learning techniques. In this role, you will lead the setup and management of IT infrastructure, develop automation tools, implement CI/CD pipelines, and ensure the security and performance of our AI models.
Key Responsibilities:
- Infrastructure Management: Set up and manage the necessary IT infrastructure, including cloud services, servers, and storage, to support the development and deployment of generative AI models.
- Automation Development: Develop and deploy automation tools and scripts to streamline repetitive tasks, such as system updates, backups, and scaling of resources.
- CI/CD Implementation: Implement continuous integration and continuous deployment (CI/CD) pipelines to automate the deployment and update processes for generative AI models.
- Security Implementation: Implement and maintain security measures to protect data and AI models from unauthorized access, breaches, and other threats.
- Monitoring Solutions: Implement monitoring solutions to continuously track the performance, availability, and health of IT systems and AI models.
- Certifications: Microsoft Azure Certifications at Associate/ Expert/ Specialty Level will be good to have. However, AZ-400 Certified candidate will be preferred for this role.
Qualifications:
- Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field.
- 3-8 years of experience in MLOps, DevOps, or a related role.
- Strong analytical and problem-solving skills.
- Excellent communication and teamwork abilities.
- Proven ability to lead and mentor team members.
- Eagerness to learn and adapt to new technologies and methodologies.
Must-Have Skills:
- Deployment for MLOps/AIOps: Extensive experience in deploying and managing machine learning/AI operations.
- Docker and Kubernetes: Proficiency in containerization and orchestration using Docker and Kubernetes.
- GIT: Strong knowledge of GIT for source control.
- Machine Learning Techniques: Hands-on experience with Regression (OLS, Logistic, Time Series), Classification, Clustering, and Decision Trees (Boosting, Bagging, etc.).
- Exposure to CI/CD: Familiarity with continuous integration and continuous deployment practices, tools like Jenkins, and Azure DevOps.
Job Type: Full-time
Benefits:
- 401(k)
- Dental insurance
- Health insurance
- Paid time off
- Referral program
- Relocation assistance
- Vision insurance
Compensation package:
- Bonus opportunities
- Performance bonus
Experience level:
- 3 years
- 4 years
- 5 years
- 6 years
- 7 years
- 8 years
Schedule:
- Day shift
- Monday to Friday
Experience:
- MLOps: 2 years (Required)
- AIOps: 2 years (Preferred)
- CI/CD: 2 years (Preferred)
- Azure: 2 years (Required)
- Docker: 1 year (Preferred)
- Kubernetes: 1 year (Preferred)
- AI chatbots: 1 year (Preferred)
- Deep learning: 1 year (Required)
- Python: 3 years (Required)
Work Location: Remote