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Location: San Diego, CA

Duration: 10+ Months

Total Hours/week: 40

1st Shift

Client: Medical Devices Company

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 scalable, cost-efficient, reliable, and secure ML solutions.

Design, implement, and deploy ML solutions in AWS.

Select and justify appropriate ML technologies within AWS and identify suitable AWS services for implementation.

Design, build, and maintain infrastructure for efficient development, deployment, and monitoring of ML models.

Implement CI/CD pipelines for ML applications to facilitate smooth development and deployment.

Collaborate with data scientists to understand and implement requirements for model serving, versioning, and reproducibility.

Monitor and optimize model performance in production, proactively resolving issues.

Automate repetitive tasks to improve efficiency and reduce human error in MLOps workflows.

Maintain documentation and provide training on MLOps best practices.

Stay updated with the latest MLOps tools, technologies, and methodologies.

Qualifications

Bachelor's or Master's degree in Computer Science, Engineering, or related field.

3+ years of experience in MLOps, DevOps, or related fields.

Strong programming skills in Python, GoLang; experience with Java, C++, or Scala is a plus.

Experience with ML frameworks such as TensorFlow, PyTorch, scikit-learn.

Proficiency with CI/CD tools like 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.

Understanding of the machine learning lifecycle, including data preprocessing, training, evaluation, and deployment.

Excellent problem-solving skills and ability to work independently and in teams.

Strong communication skills for explaining technical concepts to non-technical stakeholders.

Preferred Qualifications

AWS Certified Machine Learning - Specialty

Experience with feature stores, model registries, and monitoring tools like MLflow, Tecton, or Seldon.

Familiarity with data engineering tools such as AWS EMR, Glue, and Apache Spark.

Knowledge of security best practices for ML systems.

Experience with A/B testing and model performance monitoring.

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