MLOps Engineer - Remote [Urgent need]

MillenniumSoft Inc; San Diego (CA); Remote; USD 90,000 - 150,000; Full time

Job summary

An innovative firm is seeking a skilled MLOps Engineer to lead the operationalization of machine learning workloads. In this role, you will design, build, and maintain the infrastructure necessary for efficient development and deployment of ML models. Collaborating closely with data scientists, you will ensure that models are reliable and scalable, while also implementing CI/CD pipelines to streamline processes. If you're passionate about enhancing model performance and staying updated with the latest MLOps technologies, this is the perfect opportunity to make a significant impact in a forward-thinking environment.

Qualifications

• 3+ years of experience in MLOps or DevOps roles.
• Strong programming skills in Python and GoLang.

Responsibilities

• Design and implement scalable ML solutions in AWS.
• Collaborate with data scientists to ensure model performance.

Skills

MLOps

DevOps

Python

GoLang

Java

C++

Scala

Problem-solving

Communication

Education

Bachelor's degree in Computer Science

Master's degree in Engineering

Tools

AWS

TensorFlow

PyTorch

scikit-learn

Github Actions

Docker

Kubernetes

AWS CDK

CloudFormation

Job description

Position: MLOps Engineer - Remote

Location: San Diego, CA

Duration: 10 Months

Total Hours/week: 40.00

1^st Shift

Client: Medical Devices Company

Job Category: Technical/Engineering

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)

Job Description:

• We're seeking an experienced MLOps Engineer to lead the operationalization of our Machine Learning workloads. As a key team member, you'll be responsible for designing, building, and maintaining infrastructure required for efficient development, deployment, and monitoring of machine learning workloads.
• Your close collaboration with data scientists will ensure that our models are reliable, scalable, and performing optimally.
• This role requires expertise in automating ML workflows, enhancing model reproducibility, and ensuring continuous integration and delivery.

Responsibilities:

• 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.

Qualifications:

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
• 3+ years of experience in MLOps, DevOps, 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 TensorFlow, 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 CloudFormation.
• 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.

Preferred Qualifications:

• 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.