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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning (MLOps) Engineer - **Company:** Cox powered by Atrium - **Location:** New York, NY, United States - **Experience:** Experienced - **Salary:** $140,000.0 - $190,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Microsoft Azure, Cloud Computing, Information Engineering, DevOps, Distributed Systems, Github, Python (Programming Language), Machine Learning, Tensorflow, Prometheus, Software Deployment, Workflow Management Systems, Datadog, Data Logging, Google Cloud, Cloud Platform System, Pytorch, Snowflake, Grafana, Multi-Agent Systems, Apache Spark, Reliability of Systems, Generative AI, Cloudformation, Gitlab-ci, Scikit Learn, Kubernetes, Infrastructure Automation Frameworks, Information Technology, Performance Monitor, Apache Kafka, Machine Learning Operations, Hardware Infrastructure, Virtual Agents, Terraform, Docker, Elk Stack, Jenkins, Microservices - **Published:** May 19, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=8eb713b8997d9d8b ## About the Role Do you have experience in System performance monitoring?, Do you have a Master's degree?, * 4+ years of experience in Machine Learning Engineering, MLOps, DevOps, or Platform Engineering roles. * Strong experience deploying and managing ML models in production environments. * Hands-on expertise with Python and ML frameworks such as TensorFlow, PyTorch, or Scikit-learn. * Experience building CI/CD pipelines using tools such as GitHub Actions, Jenkins, GitLab CI, or ArgoCD. * Proficiency with Docker, Kubernetes, and container orchestration platforms. * Strong cloud experience with AWS, Azure, or Google Cloud Platform. * Experience with ML lifecycle and orchestration tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or Airflow. * Familiarity with Infrastructure-as-Code tools, including Terraform or CloudFormation. * Strong understanding of distributed systems, APIs, microservices, and production monitoring. * Experience with logging and observability tools such as Prometheus, Grafana, Datadog, or ELK Stack. * Strong communication and cross-functional collaboration skills. Preferred Experience/Skills for the Machine Learning (MLOps) Engineer: * Experience supporting Generative AI, LLMOps, or Agentic AI platforms. * Familiarity with vector databases, RAG pipelines, and AI orchestration frameworks. * Experience working in highly regulated environments such as finance, healthcare, or enterprise SaaS. * Knowledge of data engineering technologies such as Spark, Kafka, or Snowflake. * Exposure to GPU infrastructure and model optimization techniques. * Experience implementing security and governance controls for AI/ML systems. * Kubernetes certifications or cloud platform certifications are preferred. Education Requirements: * Bachelor's degree in Computer Science, Engineering, Data Science, Information Technology, or a related technical field is required. * Master's degree is preferred. ## Description Our client is seeking a highly skilled Machine Learning (MLOps) Engineer to support the deployment, automation, monitoring, and scalability of enterprise machine learning systems. This role will partner closely with Data Scientists, Software Engineers, DevOps teams, and business stakeholders to operationalize ML models in production environments. The ideal candidate has strong experience building CI/CD pipelines for ML workflows, managing cloud-native infrastructure, and supporting end-to-end machine learning lifecycle management., * Design, build, and maintain scalable MLOps platforms and infrastructure for machine learning model deployment and monitoring. * Develop and automate CI/CD pipelines for ML training, testing, validation, and production deployment. * Collaborate with Data Scientists and Engineering teams to productionize machine learning models and workflows. * Implement model versioning, experiment tracking, feature stores, and automated retraining pipelines. * Monitor model performance, drift detection, system reliability, and operational health across production environments. * Manage cloud infrastructure and containerized applications using Kubernetes, Docker, and Infrastructure-as-Code tools. * Optimize ML workflows for scalability, performance, security, and cost efficiency. * Support governance, compliance, and reproducibility standards for enterprise AI systems. * Troubleshoot infrastructure, deployment, and model performance issues across distributed systems. * Contribute to platform engineering best practices, automation strategies, and operational documentation. ## Related Videos - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) - [MLOps - What’s the deal behind it?](https://www.wearedevelopers.com/videos/392-mlops-what-s-the-deal-behind-it) ## Related Articles - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path)