Ml-ops Engineer Job in Mobio Solutions
Job Summary
Responsibilities:
Design and implement large-scale ML systems to support training and serving
workloads.
Collaborating and share knowledge with our cloud ops team to compress time-to-
production for Machine Learning.
Build tooling and pipelining abstractions to allow Data scientists to focus on
experimentation while empowering self-service workflows to deploy and serve
models reliably and consistently.
Help Data Scientists produce clean, reproducible, and highly performant machine
learning systems through rigorous code review with a lens on software quality.
Advocate for automation and monitoring at all steps of ML system construction, and
help to define best practices based on personal industry experience and research
across the Machine Learning team.
Support life cycle management of deployed ML apps (e.g., new releases, change
management, monitoring and troubleshooting).
Participate in sprint planning, estimations, and reviews.
Qualifications:
3+ years of software development experience, preferably in Python.
Experience with maintaining functional, production reference architectures for end-
to-end Machine Learning in cloud.
Familiarity with ML-Ops tools and platforms such as Vertex AI, MLFlow and DVC.
Strong Linux system administration skills.
Experience with declarative infrastructure and Kubernetes (GKE) for model serving
and scalable inference.
Exposure to automated testing and CI/CD in the ML context.
Knowledge of SQL and relational databases, query authoring (SQL) and designing
variety of databases (e.g., Postgres SQL).
Understanding of fundamental ML concepts.
Interest in continually learning and trying new tools.
Strong cross-team communication and collaboration skills.
Design and implement large-scale ML systems to support training and serving
workloads.
Collaborating and share knowledge with our cloud ops team to compress time-to-
production for Machine Learning.
Build tooling and pipelining abstractions to allow Data scientists to focus on
experimentation while empowering self-service workflows to deploy and serve
models reliably and consistently.
Help Data Scientists produce clean, reproducible, and highly performant machine
learning systems through rigorous code review with a lens on software quality.
Advocate for automation and monitoring at all steps of ML system construction, and
help to define best practices based on personal industry experience and research
across the Machine Learning team.
Support life cycle management of deployed ML apps (e.g., new releases, change
management, monitoring and troubleshooting).
Participate in sprint planning, estimations, and reviews.
Qualifications:
3+ years of software development experience, preferably in Python.
Experience with maintaining functional, production reference architectures for end-
to-end Machine Learning in cloud.
Familiarity with ML-Ops tools and platforms such as Vertex AI, MLFlow and DVC.
Strong Linux system administration skills.
Experience with declarative infrastructure and Kubernetes (GKE) for model serving
and scalable inference.
Exposure to automated testing and CI/CD in the ML context.
Knowledge of SQL and relational databases, query authoring (SQL) and designing
variety of databases (e.g., Postgres SQL).
Understanding of fundamental ML concepts.
Interest in continually learning and trying new tools.
Strong cross-team communication and collaboration skills.
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