Design, build, and improve machine learning solutions in a cloud environment, moving from prototype to production. The role involves exploring data, running experiments, and implementing reliable, reproducible workflows for real business problems.
Remote ML Ops Engineer Jobs (13)
Search = ML Ops Engineer
1935 Azure DevOps Engineer (AI / Agentic) ML Ops Engineer
In All Media Inc
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Full Time
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1 Week Ago
In All Media Inc
Design and implement AI-driven automation and LLM integrations to optimize DevOps workflows and reduce human toil. Architect and manage scalable Azure cloud infrastructure, focusing on self-healing ecosystems and high availability.
Build and operate a model-serving platform and take end-to-end ownership of ML systems from requirement gathering to production monitoring. Collaborate with customers to develop scalable AI solutions and optimize inference APIs for performance and GPU utilization.
AI Commercial & ML Ops Engineer
MGM Resorts International
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Full Time
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4 Weeks Ago
MGM Resorts International
Design and optimize scalable AI/ML deployment pipelines to automate the full machine learning lifecycle from ingestion to retraining. Partner with data teams to ensure models are production-ready, secure, and performant across cloud platforms.
Architect and operate end-to-end ML pipelines for training and deployment on GCP and AWS. Maintain system monitoring, alerting, and CI/CD automation for ML artifacts and infrastructure.
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ML Ops Engineer ML Ops Engineer
Nestle Operational Services Worldwide SA
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Full Time
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6 Weeks Ago
Nestle Operational Services Worldwide SA
The role focuses on optimizing, standardizing, and implementing scalable machine learning solutions within Azure cloud environments. Responsibilities include managing the end-to-end ML lifecycle, designing monitoring pipelines, and collaborating with data engineering to deploy models in production.
You will own the MLOps infrastructure, designing and scaling systems to transition research models into production-ready environments. This includes managing training pipelines, cloud architecture, and CI/CD processes to ensure reliable model performance.
You will architect and build the core infrastructure, APIs, and distributed systems that power AI/ML workloads at scale. You will collaborate with ML engineers to translate research prototypes into robust, production-ready systems.
You will design and operate scalable, production-grade model serving infrastructure for AI applications. This includes managing deployment pipelines, optimizing GPU utilization, and ensuring high availability for real-time inference systems.
The Senior MLOps Engineer will own the end-to-end ML lifecycle, including model packaging, deployment, monitoring, and optimization for a custom inference platform powering a conversational shopping agent. Responsibilities include building and optimizing production-grade ML pipelines and defining strategies for model versioning, rollout, and lifecycle management.
The role involves scaling the decision-making process for the tvScientific AI team's tools, covering workflows, training infrastructure, and Kubernetes deployments. Responsibilities also include improving the data science team's developer experience and serving as a technical lead and mentor.
The role involves building, maintaining, and improving Torc's ML frameworks, focusing on cloud tooling, data operations, and the ML development pipeline. This includes designing organization-wide ML Ops systems and driving technical requirements for internal stakeholders.
You will design and maintain CI/CD pipelines and deployment infrastructure to transition ML models from research to production. Additionally, you will implement monitoring, A/B testing, and optimization strategies to ensure model performance and reliability at scale.
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