MLOps Engineer - Remote; AWS Certified Machine Learning

Overview

Join to apply for the MLOps Engineer - Remote (AWS Certified Machine Learning) role at Millennium Soft Inc
.

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)

• 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.

• 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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