Dice
Posted 37 days agoVerified live 1d ago
MLOps Engineer // REMOTE / Work-Life Balance
Brief overview
Remote
UndergradOr in progress
3+ yrsMinimum
Machine Learning Operations (MLOps)Machine Learning Model DeploymentPythonAWSDatabricksCI/CD PipelinesDockerKubernetesMonitoring and ObservabilityIncident ResponseLarge Language Models (LLMs)Generative AI
About the company
Dicedice.com
Dice is the go-to career marketplace for tech professionals.
Job description
Summary
The hiring organization is an industry-leading technology and analytics organization investing in artificial intelligence innovation. The AI/ML Operations Engineer will deploy and operate machine learning solutions, build cloud infrastructure, manage containerized environments, automate CI/CD processes, and support reliable production AI systems across enterprise environments.
Responsibilities
- 40% Cloud Infrastructure (AWS, Databricks)
- 25% Kubernetes & Container Management
- 20% CI/CD & Deployment Automation
- 15% Monitoring, Observability & Reliability
- 80% Hands On Engineering
- 5% Strategic Planning
- 15% Team Collaboration
Skills
- 3+ years of experience in MLOps, Platform Engineering, or Infrastructure Engineering
- Experience deploying machine learning models into production environments
- Strong Python development and scripting skills
- Hands-on experience with AWS cloud services
- Experience with Databricks or comparable data/AI platforms
- Experience building and maintaining CI/CD pipelines
- Docker and Kubernetes experience
- Monitoring, observability, and incident response experience
- Bachelor's Degree in Computer Science, Engineering, or related field (or equivalent experience)
- Applicants must be currently authorized to work in the US on a full-time basis now and in the future
- Exposure to large language models (LLMs) and generative AI systems
- Experience with AI cost optimization and resource management
- SQL and data analytics experience
- Infrastructure-as-Code experience
- Model versioning and reproducibility knowledge
- Experience supporting enterprise-scale AI workloads
Qualifications
Must Haves
- 3+ years of experience in MLOps, Platform Engineering, or Infrastructure Engineering
- Experience deploying machine learning models into production environments
- Strong Python development and scripting skills
- Hands-on experience with AWS cloud services
- Experience with Databricks or comparable data/AI platforms
- Experience building and maintaining CI/CD pipelines
- Docker and Kubernetes experience
- Monitoring, observability, and incident response experience
- Bachelor's Degree in Computer Science, Engineering, or related field (or equivalent experience)
- Applicants must be currently authorized to work in the US on a full-time basis now and in the future
Nice to Haves
- Exposure to large language models (LLMs) and generative AI systems
- Experience with AI cost optimization and resource management
- SQL and data analytics experience
- Infrastructure-as-Code experience
- Model versioning and reproducibility knowledge
- Experience supporting enterprise-scale AI workloads
Benefits
- Medical, Dental, and Vision Insurance
- Career Development Opportunities
- Remote work
- Work-life balance
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