Selected: ✨ MLOps Engineer.
Selected: 🆕 Date Added.
🕒 3 days ago
MLOps Engineer building scalable machine learning infrastructure and deployment pipelines for ShuruTech’s global IT services clients. Managing cloud ML platforms, model monitoring, and production deployments.
AWS
Azure
Cloud
Docker
Google Cloud Platform
Kubernetes
Python
PyTorch
Scikit-Learn
Tensorflow
🕒 4 days ago
MLOps Engineer deploying, versioning, and monitoring machine learning models in production. Automating reliable ML infrastructure with Python, CI/CD, containers, and cloud platforms.
AWS
Azure
Cloud
Docker
Google Cloud Platform
Kubernetes
Python
🕒 5 days ago
Staff AI/MLOps Engineer projetando plataformas AWS de MLOps e GenAI. Acelerando modelos de IA no marketplace digital de classificados líder do Grupo OLX.
🗣️🇧🇷🇵🇹 Portuguese Required
AWS
🕒 September 21
Senior MLOps Engineer building GCP infrastructure, deployment pipelines, and observability for production AI models. Point Wild delivers cybersecurity solutions protecting customers’ identities and personal information.
Airflow
Cloud
Docker
Google Cloud Platform
Grafana
Kubernetes
Microservices
Prometheus
Python
SQL
Terraform
🕒 September 21
Senior MLOps Engineer building GCP infrastructure, deployment pipelines, and observability for Point Wild’s cybersecurity solutions. Scaling AI models from prototypes into reliable production systems.
Airflow
Cloud
Docker
Google Cloud Platform
Grafana
Kubernetes
Microservices
Prometheus
Python
SQL
Terraform
🕒 September 21
Senior MLOps Engineer building GCP infrastructure and production ML systems. Scaling reliable AI capabilities for Point Wild’s cybersecurity protection products.
Airflow
Cloud
Docker
Google Cloud Platform
Grafana
Kubernetes
Microservices
Prometheus
Python
SQL
Terraform
🕒 September 21
Senior MLOps Engineer building GCP infrastructure, deployment pipelines, and observability for production AI. Supporting Point Wild’s cybersecurity solutions through reliable, scalable machine learning systems.
Airflow
Cloud
Docker
Google Cloud Platform
Grafana
Kubernetes
Microservices
Prometheus
Python
SQL
Terraform
🕒 September 21
Senior MLOps Engineer building scalable GCP infrastructure and ML deployment pipelines. Enabling Point Wild’s cybersecurity solutions to run reliably in production.
Airflow
Cloud
Docker
Google Cloud Platform
Grafana
Kubernetes
Microservices
Prometheus
Python
SQL
Terraform
🕒 September 21
Senior MLOps Engineer building and operating GCP infrastructure for Point Wild’s cybersecurity AI solutions. Deploying, scaling, and monitoring production machine-learning models.
Airflow
Cloud
Docker
Google Cloud Platform
Grafana
Kubernetes
Microservices
Prometheus
Python
SQL
Terraform
🕒 September 21
Senior MLOps Engineer building GCP infrastructure and ML deployment pipelines. Operationalizing AI models for Point Wild’s cybersecurity protection products.
Airflow
Cloud
Docker
Google Cloud Platform
Grafana
Kubernetes
Microservices
Prometheus
Python
SQL
Terraform
🕒 September 18
MLOps Engineer creating and evaluating training data for a leading AI lab's GenAI systems. Focusing on GPU kernels, profiling, distributed debugging, and high-throughput LLM serving.
🌐 United States, Canada, +1 more countries – Remote
💵 $90 - $120 / hour
⏱ Part Time
🟢 Junior
🟡 Mid-level
🗣️ LLM Engineer
🚫👨🎓 No degree required
👻 Ghost score 20%
Distributed Systems
PyTorch
Ray
🕒 September 17
MLOps Engineer building eMAG’s scalable Google Cloud ML platform. Automating ML workflows, infrastructure, observability, and model deployment across the organization.
AWS
Azure
BigQuery
Cloud
Consul
Google Cloud Platform
Grafana
Java
Kubernetes
Linux
PHP
Prometheus
Python
Ruby
Shell Scripting
SQL
Terraform
Vault
🕒 September 16
Senior LLMOps Engineer operationalizing Citrin Cooperman’s generative AI applications. Building CI/CD, evaluation, observability, guardrail, and cost-management infrastructure.
🇺🇸 United States – Remote
💵 $155k - $195k / year
⏰ Full Time
🟠 Senior
👷🏻♀️ Engineer
🦅 H1B Visa Sponsor
👻 Ghost score 0%
AWS
Azure
Docker
Kubernetes
Python
🕒 September 15
Tech Lead building production MLOps pipelines and infrastructure for Megazone Cloud’s cloud services. Driving SageMaker, Terraform, monitoring, compliance, and technical architecture.
🇺🇸 United States – Remote
💵 $80 - $120 / hour
⏳ Contract/Temporary
🟠 Senior
🧑💻 Full-stack Engineer
👻 Ghost score 4%
Terraform
🕒 September 15
Senior MLOps Engineer building production-grade ML platforms for Sigma Software’s high-load programmatic advertising ecosystem. Automating model orchestration, lifecycle management, observability, and real-time optimization.
Airflow
Cloud
Docker
Google Cloud Platform
Kubernetes
Linux
Python
Terraform
🕒 September 14
Senior Back-End/MLOps Engineer building search, ranking, and matching systems for a real-estate technology platform. Supporting production ML pipelines, model deployment, and real-time data infrastructure.
AWS
Azure
Cloud
Google Cloud Platform
JavaScript
Python
Ruby
SQL
TypeScript
Go
🕒 September 14
Senior Back-End/MLOps Engineer building search, ranking, and matching systems for a real-estate rental platform. Supporting production ML pipelines, feature stores, and real-time infrastructure.
AWS
Azure
Google Cloud Platform
JavaScript
Python
Ruby
SQL
TypeScript
Go
🕒 September 14
Senior Back-End/MLOps Engineer building marketplace search, ranking, and ML infrastructure. Supporting a real-estate platform that simplifies finding and renting homes.
AWS
Azure
Cloud
Google Cloud Platform
JavaScript
Python
Ruby
SQL
TypeScript
Go
🕒 September 14
Senior Back-End/MLOps Engineer building search, ranking, and ML infrastructure for a real-estate technology platform. Supporting production machine learning systems and scalable marketplace experiences.
AWS
Azure
Google Cloud Platform
JavaScript
Python
Ruby
SQL
TypeScript
Go
🕒 September 11
MLOps Engineer operating scalable ML infrastructure for SumerSports’ football intelligence platform. Deploying production models, optimizing cloud workloads, and enabling NFL and NCAA insights.
🇺🇸 United States – Remote
💵 $170k - $200k / year
⏰ Full Time
🟡 Mid-level
🟠 Senior
🏗️ Platform Engineer
👻 Ghost score 0%
AWS
Azure
Cloud
Google Cloud Platform
GRPC
Kubernetes
Python
Spark
+59 More MLOps Engineer Jobs Available!
The average salary for remote mlops engineers is $160,608 per year. This is based on data from 185 job openings. Our advanced AI searches the internet for remote job openings and posts them on our website. We use the salary data from these job postings to calculate salary expectations.
Below is a breakdown of salary data by years of experience:
| Experience | Number of roles analyzed | Average Salary |
|---|---|---|
🟢 Junior MLOps Engineer (1-2 yrs) | 9 | $128,238 |
🟡 Mid-level MLOps Engineer (2-4 yrs) | 74 | $139,141 |
🟠 Senior MLOps Engineer (5-9 yrs) | 81 | $171,527 |
🔴 Lead MLOps Engineer (10+ yrs) | 21 | $208,008 |
We analyzed 134 job listings in the last year and found it takes about 40 days for employers to close a job opening.
We reviewed 185 job postings and found the top 10 skills employers are asking for most often are:
You need strong programming skills in Python or R, expertise in machine learning frameworks like TensorFlow or PyTorch, proficiency in cloud services (AWS, Azure, GCP), and knowledge of CI/CD practices. Familiarity with data pipelines, containerization (Docker, Kubernetes), and strong problem-solving skills are also important.
Typically, a degree in computer science, data science, or a related field is required. Experience with machine learning projects and a strong portfolio of work are often more important than formal education. Certifications in cloud platforms and MLOps methodologies can enhance your qualifications.
Responsibilities include deploying machine learning models, automating workflows, monitoring model performance, collaborating with data scientists and developers, and ensuring scalability and reliability of ML applications. You'll also need to maintain data integrity and adherence to best practices.
Benefits include flexibility in work hours, the ability to work from diverse locations, reduced commuting costs and time, and an enhanced work-life balance. Remote roles also provide opportunities to work with cutting-edge technologies and collaborate with a global talent pool.
We scan the internet everyday and find jobs not posted on LinkedIn or other job boards.
We find jobs minutes after they're posted, so you can apply before everyone else.
Most members hear back within the first week
We find jobs other job boards miss.