AI/Machine Learning Ops Engineer

Education and Experience »
Proven engineering background. A technical degree in a field such as Computer Science or Data Analytics, and/or equivalent work experience.
Knowledge of standard ML/AI libraries. For example, Pandas, scikit-learn, TensorFlow/PyTorch/JAX, Kubeflow, etc.
Proficiency in SQL. Knowledge of SQL is occasionally required for data ingestion and analysis.

Preferred Qualifications:
Fluent in Python. Fluent candidates can comfortably: read and write Python code; write method and class docstrings; manage package dependencies in a principled way.
Comfortable with git. Use GitHub extensively to develop and deploy code; candidates should be comfortable writing quality PRs, utilizing GitOps, etc.
Strong command lines skills in *nix environments.
Excellent understanding of networking and security.
Experienced in IaC and cloud deployment. Deploy applications to private and public cloud environments using cloud-native tooling and IaC principles (Terraform, Argo CD, Istio, etc.).
Familiar with ML/AI concepts and practices. Candidates should be comfortable with concepts such as model development, training, and monitoring.

WHAT YOU’LL DO:
Job Responsibilities:
Enjoys learning across the tech stack, from new developments in DevSecOps and automation to machine learning and artificial intelligence.
Take pride in their work and derives great satisfaction from building reliable and maintainable infrastructure to support team.
Isn't afraid to voice opinions, propose solutions, and receive constructive criticism in a collaborative design process.
Is a social coder who likes sharing and receiving code reviews, and values pair programming where appropriate.
Thrives in a remote-first work environment and uses remote collaboration tools effectively.
They should be comfortable with writing and deploying the applications.
Someone who can log and monitor the code.