MillenniumSoft Inc
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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.