> Markdown version of [/jobs/ext/594747-remote-machine-learning-engineer](https://www.wearedevelopers.com/jobs/ext/594747-remote-machine-learning-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # REMOTE Machine Learning Engineer - **Company:** Insight Global - **Location:** Colorado Springs, CO, United States - **Experience:** Expert - **Contract:** Temporary to permanent - **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Business Logic, Microsoft Azure, Cloud Computing, Cloud Engineering, Continuous Integration, Data Validation, Information Engineering, Data Structures, DevOps, Fraud Prevention and Detection, Python (Programming Language), Machine Learning, Recommender Systems, Tensorflow, Azure Machine Learning, Salesforce.Com, Sentiment Analysis, Software Engineering, SQL Databases, Oracle Fusion Middleware, Management of Software Versions, Enterprise Data Management, Pytorch, Snowflake, Scikit Learn, Kubernetes, Deployment Automation, Xgboost, Feature Selection, Machine Learning Operations, Databricks - **Published:** June 21, 2026 - **Apply:** https://www.juju.com/job/00000000g9nusi ## About the Role This will be a contract to hire opportunity, with the ability to sit remotely anywhere in the US., 8+ years of experience in machine learning, data science, or AI engineering, with a strong software engineering background -Proficient in Python, and libraries such as scikit-learn, XGBoost, PyTorch, TensorFlow, or similar -Experience deploying models into production using ML pipelines and orchestration frameworks -Strong understanding of data structures, SQL, and cloud platforms (AWS SageMaker, Azure ML, or GCP Vertex AI) -Familiarity with MLOps tools (MLflow, SageMaker Pipelines, Feature Store). -Exposure to enterprise data platforms (e.g., Snowflake, Oracle Fusion, Salesforce). -Background in statistics, forecasting, optimization, or recommendation systems. ## Description Insight Global is looking for a Machine Learning Engineer to help lead the design, development, and deployment of scalable machine learning models that power business decisions across this company's enterprise. This role combines technical depth in ML/AI with a strong understanding of business domains such as Sales, Service, Finance, Order Fulfillment, and Supply Chain. You will collaborate closely with Data Scientists, Data Engineers, and business partners to build production-ready solutions that drive measurable impact. Responsibilities: Machine Learning Development & Deployment -Design and implement supervised and unsupervised models for predictive analytics, including churn prediction, demand forecasting, renewal risk scoring, and cross-sell/upsell opportunity identification. -Translate business problems into ML frameworks and production solutions that improve efficiency, revenue, or customer experience. -Build, optimize, and maintain ML pipelines using tools such as MLflow, Airflow, or Kubeflow. Cross-Functional ML Use Cases -Partner with teams across Sales (e.g., lead scoring, next-best action), Customer Service (e.g., case deflection, sentiment analysis), Finance (e.g., revenue forecasting, fraud detection), Supply Chain (e.g., inventory optimization, ETA prediction), and Order Fulfillment (e.g., delivery risk modeling) to define impactful ML use cases. -Develop domain-specific models and continuously improve them using feedback loops and real-world performance data. Model Governance and MLOps -Ensure robust model monitoring, versioning, and retraining strategies to keep models reliable in dynamic environments. -Work closely with DevOps and Data Engineering teams to automate deployment, CI/CD workflows, and cloud-native ML infrastructure (AWS/GCP/Azure). Data Engineering and Feature Architecture -Collaborate with data engineers to define feature stores, data quality checks, and model-ready datasets on platforms like Snowflake or Databricks. -Perform feature selection, transformation, and engineering aligned with each domain's business logic. Communication & Stakeholder Collaboration -Present technical insights and model results to business and executive stakeholders in a clear, actionable format. -Work with Product Owners and Program Managers to scope, prioritize, and plan delivery of ML projects. ## Related Videos - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [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) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [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) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Fully Remote Software Engineer Jobs](https://www.wearedevelopers.com/magazine/447-fully-remote-software-engineer-jobs) - [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)