MLOps Engineer | Sunnyvale CA | C2C | W2 | FTE | Long Term

 

Certainly! Here's a detailed job description for an MLOps Engineer role based in Sunnyvale, CA:

Job Role: MLOps Engineer

Location: Sunnyvale, CA

Duration: Long Term

Job Type: C2C/W2/FTE (Contract/Full-Time Equivalent)

Job Description:

We are seeking a talented MLOps Engineer to join our team in Sunnyvale, CA. As an MLOps Engineer, you will play a crucial role in deploying, managing, and optimizing machine learning models and pipelines in production environments. You will collaborate with data scientists, software engineers, and IT teams to ensure the reliability, scalability, and performance of machine learning solutions.

Key Responsibilities:

1. Model Deployment: Design, build, and deploy machine learning models into production environments using automation and containerization techniques.

2. Infrastructure Management: Set up and maintain infrastructure for machine learning projects, including cloud environments (e.g., AWS, GCP, Azure) and on-premises clusters.

3. Pipeline Automation: Implement CI/CD pipelines for automated model training, testing, deployment, and monitoring.

4. Monitoring and Logging: Develop monitoring and logging solutions to track model performance, data quality, and system health.

5. Security and Compliance: Ensure data security and compliance with regulations (e.g., GDPR, HIPAA) throughout the machine learning lifecycle.

6. Collaboration: Work closely with cross-functional teams, including data scientists, software engineers, and DevOps, to streamline workflows and improve productivity.

7. Documentation: Create and maintain documentation for infrastructure, deployment processes, and best practices.

8. Continuous Improvement: Stay updated with the latest trends and technologies in MLOps and recommend improvements to enhance the efficiency and reliability of machine learning systems.

Required Skills and Qualifications:

• Programming Skills: Proficiency in programming languages such as Python, Java, or Scala.
• Containerization: Experience with Docker and container orchestration tools (e.g., Kubernetes).
• Cloud Platforms: Knowledge of cloud services and platforms (AWS, GCP, Azure) for deploying and managing machine learning solutions.
• CI/CD: Familiarity with CI/CD pipelines and automation tools (e.g., Jenkins, GitLab CI/CD).
• Version Control: Experience with version control systems (e.g., Git) for managing code and configurations.
• Monitoring Tools: Familiarity with monitoring and logging tools (e.g., Prometheus, ELK stack).
• Problem-Solving: Strong analytical and problem-solving skills with a focus on delivering reliable and scalable solutions.
• Communication: Excellent communication and collaboration skills to work effectively within a team environment.

Preferred Qualifications:

• Education: A bachelor’s degree in Computer Science, Engineering, or a related field; a master’s degree is a plus.
• Experience: Previous experience in MLOps, DevOps, or related roles in deploying and managing machine learning models in production environments.
• Certifications: Certifications in cloud platforms (e.g., AWS Certified DevOps Engineer, Google Professional Cloud DevOps Engineer).

Job Type and Duration:

• Contract (C2C/W2): Specifies whether the role is contract-based, with options for Corp-to-Corp (C2C) or as an employee (W2).
• Full-Time Equivalent (FTE): Indicates whether the role is also open for permanent employment.
• Duration: Listed as "Long Term," indicating the expectation for a sustained engagement rather than short-term.

This role offers an exciting opportunity to work at the intersection of machine learning and operations, contributing to the deployment and optimization of cutting-edge AI solutions in a collaborative and innovative environment.

Contact Information:

For more details or to apply, please contact eakansh.srivastava@dataisgood.com

This contact information is essential for applicants to reach out and inquire further about the role or to apply directly.