MLOps Engineer - Remote

Overview

Join to apply for the MLOps Engineer - Remote [Urgent need] role at Millennium Soft Inc

Level of

Experience:

Senior Level

Employment Type:

Contract on W2 (Need US Citizens or GC Holders or GC EAD or OPT or EAD or CPT)

Location:

San Diego, CA

Duration: 10 Months

Total Hours per week: 40.00

1st Shift

Client:
Medical Devices Company

Job Category:
Technical/Engineering

Seniority level:
Mid-Senior level

Employment type:

Full-time (note: the original listing specifies Contract on W2; see details above)

• Architect for scalable, cost-efficient, reliable and secure ML solution.
• Design, implement and deploy ML solutions in AWS.
• Select and justify appropriate ML technology within AWS and identify appropriate AWS services to implement ML solutions.
• Design, build, and maintain infrastructure required for efficient development, deployment, and monitoring of machine learning models.
• Implement CI/CD pipelines for machine learning applications to ensure smooth development and deployment processes.
• Collaborate with data scientists to understand and implement requirements for model serving, versioning, and reproducibility.
• Monitor and optimize model performance in production, identifying and resolving issues proactively to ensure optimal results.
• Automate repetitive tasks to improve efficiency and reduce the risk of human error in MLOps workflows.
• Maintain documentation and provide training to team members on MLOps best practices, ensuring knowledge sharing and collaboration within the team.
• Stay updated with the latest developments in MLOps tools, technologies, and methodologies to remain current and effective in your role.

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
• 3+ years of experience in MLOps, Dev Ops, or related fields.
• Strong programming skills in Python, GoLang with experience in other languages such as Java, C++, or Scala being a plus.
• Experience with ML frameworks such as Tensor Flow, PyTorch, and/or scikit-learn.
• Proficiency with CI/CD tools such as Github Actions.
• Hands-on experience with AWS.
• Familiarity with containerization and orchestration tools like Docker and Kubernetes.
• Knowledge of infrastructure-as-code tools such as AWS CDK and Cloud Formation.
• Strong understanding of machine learning lifecycle, including data preprocessing, model training, evaluation, and deployment.
• Excellent problem-solving skills and the ability to work independently as well as part of a team.
• Strong communication skills and the ability to explain complex technical concepts to non-technical stakeholders.

• AWS Certified Machine Learning - Specialty
• Experience with feature stores, model registries, and monitoring tools such as MLflow, Tecton, or Seldon.
• Familiarity with data engineering tools such as AWS EMR, Glue and Apache Spark.
• Knowledge of security best practices for machine learning systems.
• Experience with A/B testing and model performance monitoring.

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