REQUIRED QUALIFICATIONS:

• Proven experience designing and implementing MLOps pipelines on cloud platforms

(Preferably GCP & AWS).

• Hands-on expertise with MLOps frameworks (e.g., Kubeflow, MLFlow, Metaflow,

Ray) and containerization tools (Docker, Kubernetes).

• Strong programming skills in Python, Bash, or similar, paired with deep knowledge of

Linux environments.

• Experience with monitoring tools like Prometheus, Grafana, or custom logging

frameworks for tracking system and model performance.

• Knowledge of distributed computing frameworks (e.g., Spark, Ray) for handling large-scale data processing or model training.
• Understanding of RESTful APIs and microservices architecture, with experience

integrating ML models into application ecosystems.

• Excellent English communication skills, with a collaborative, team-focused approach.

Preferred Qualifications:

• Experience with real-time data processing or edge computing.
• Background in AI/ML applications tied to neuroscience, wearables, or human-

computer interaction (aligned with EMOTIV’s mission).
Please share your CV to Ms Huyen at huyennguyen@emotiv.com

• Design, build, and troubleshoot production-grade AI systems and applications on

GCP & AWS

• Develop and maintain CI/CD pipelines using tools like Jenkins, GitHub Actions, or

similar.

• Optimize, refactor, containerize, deploy, and monitor data science models, ensuring

robust versioning and quality control.

• Automate testing, validation, and performance evaluation of machine learning

models.

• Partner with data scientists, engineers, and architects to deliver scalable solutions,

documenting processes clearly and comprehensively.

• Manage and optimize infrastructure-as-code (IaC) using tools like Terraform or

CloudFormation to ensure scalable and reproducible environments.

• Implement and monitor model performance metrics in production, proactively addressing drift, bias, or degradation.
• Ensure security and compliance of AI systems, including data privacy standards (e.g., GDPR, HIPAA) and secure deployment practices.