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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Contract Machine Learning Engineer - **Company:** Cox powered by Atrium - **Location:** New York, NY, United States - **Experience:** Experienced - **Salary:** $156,000.0 - $228,800.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Application Lifecycle Management, Microsoft Azure, Big Data, Cloud Computing, Cloud Engineering, Computer Programming, Continuous Integration, DevOps, Distributed Systems, Github, Machine Learning, Tensorflow, Software Engineering, Google Cloud, Pytorch, Snowflake, Multi-Agent Systems, Apache Spark, Generative AI, Cloudformation, AI Platforms, Gitlab-ci, Scikit Learn, Kubernetes, Infrastructure Automation Frameworks, Information Technology, Apache Kafka, Machine Learning Operations, Hardware Infrastructure, Terraform, Software Version Control, Docker, Jenkins - **Published:** May 20, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=899c37b58b379709 ## About the Role Do you have experience in Technical troubleshooting support?, * 4+ years of experience in MLOps, Machine Learning Engineering, DevOps, or Platform Engineering. * Strong experience deploying machine learning models into production environments. * Advanced Python programming skills and familiarity with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn. * Experience with CI/CD tools, including GitHub Actions, Jenkins, GitLab CI, or similar platforms. * Hands-on expertise with Docker, Kubernetes, and container orchestration. * Strong cloud platform experience with AWS, Azure, or Google Cloud Platform. * Experience with ML orchestration and lifecycle management tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or Airflow. * Familiarity with Infrastructure-as-Code tools such as Terraform or CloudFormation. * Strong understanding of APIs, distributed systems, and production monitoring tools. * Excellent troubleshooting, communication, and collaboration skills. Preferred Experience/Skills for the Contract Machine Learning Engineer: * Experience supporting Generative AI, LLMOps, or AI platform engineering initiatives. * Familiarity with vector databases, RAG architectures, and AI orchestration frameworks. * Experience working within enterprise or highly regulated environments. * Knowledge of Spark, Kafka, Snowflake, or large-scale data processing platforms. * Exposure to GPU infrastructure and model optimization workflows. * Cloud certifications or Kubernetes certifications preferred. Education Requirements: * Bachelor's degree in Computer Science, Engineering, Information Technology, Data Science, or a related technical discipline is preferred. ## Description Our client is seeking an experienced Contract Machine Learning Engineer to support the deployment, automation, and operationalization of machine learning platforms and production AI systems. This Contract Machine Learning Engineer will work closely with Data Science, DevOps, Cloud Engineering, and Software Development teams to streamline ML workflows, improve deployment reliability, and optimize scalable cloud infrastructure. The ideal candidate has hands-on experience supporting production ML environments and building automated MLOps pipelines., * Design, implement, and support scalable MLOps infrastructure and deployment frameworks. * Build and maintain CI/CD pipelines for machine learning model training, testing, and deployment. * Collaborate with Data Scientists and Engineering teams to productionize ML models and AI services. * Automate model monitoring, validation, retraining, and performance tracking processes. * Support containerized deployments using Kubernetes and Docker technologies. * Manage and optimize cloud-based ML environments across AWS, Azure, or GCP. * Implement model versioning, experiment tracking, and reproducibility standards. * Monitor production systems for model drift, latency, reliability, and operational issues. * Troubleshoot infrastructure and deployment-related challenges across distributed environments. * Document MLOps workflows, platform architecture, and operational best practices. ## Related Videos - [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops) - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [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) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [DevOps for Machine Learning](https://www.wearedevelopers.com/videos/179-devops-for-machine-learning) - [Bringing AI Model Testing and Prompt Management to Your Codebase with GitHub Models](https://www.wearedevelopers.com/videos/1536-bringing-ai-model-testing-and-prompt-management-to-your-codebase-with-github-models) ## Related Articles - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [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) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)