What you will do:

Help design, build and maintain ML inference infrastructure on cloud platforms like AWS and GCP.
Optimize ML pipelines for scalability, reliability, and fault tolerance.
Help maintain, expand internal Infrastructure-as-code (IaC) repositories
Leverage your experience to further cultivate a strong DevOps/MLOps culture
Collaborate with machine learning engineers to rapidly train and deploy ML models at scale

Required skills:

Strong communication skills, with the ability to communicate complex technical concepts to both technical and non-technical stakeholders.
Experience working in a collaborative software development environment that includes design and code reviews
Proficient in Python, scripting, infrastructure-as-code (CDK/Terraform)
Demonstrable knowledge of common software design patterns and principles
3-5 years of experience in data engineering, software engineering, machine learning, and/or cloud computing.
Hands on experience deploying scaled ML solutions on cloud platforms like AWS/GCP and related services.
Knowledge of machine learning pipelines / training processes, and frameworks like MLFlow
Experience in a DevOps/MLOps role, configuring and launching docker containers, CI/CD, scheduling and orchestration frameworks (argo worfklows/airflow/luigi/dagster/etc..)
Demonstrated ability to learn and adapt to new technologies, tools, and best practices

Other Requirements: