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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer - **Company:** Everforth Apex - **Location:** Plano, TX, United States - **Experience:** Expert - **Contract:** Temporary contract - **Skills:** Artificial Intelligence, Amazon Web Services, Business Analytics Applications, Data Analysis, Microsoft Azure, Big Data, Cyber Security, Continuous Integration, Data Cleansing, Data Mining, Distributed Computing Environment, Monitoring of Systems, Python (Programming Language), Machine Learning, NumPy, Standard Sql, Systems Integration, Transaction Data, Management of Software Versions, Feature Engineering, Prompt Engineering, Apache Spark, Model Validation, Generative AI, Pandas, Scikit Learn, Information Technology, Xgboost, Machine Learning Operations, Software Version Control, Data Pipelines, Databricks - **Published:** September 11, 2026 - **Apply:** https://www.dice.com/job-detail/a641e65b-3ef4-4579-a66f-b2820328e81d ## About the Role We are seeking a senior-level Machine Learning Engineer or Data Scientist to independently deliver complex classification models, from data exploration through to production implementation. The ideal candidate will have hands-on experience with Python, feature engineering, model training, and deployment of machine learning solutions. This person should be comfortable working with large datasets, building data pipelines, and partnering with engineering teams to integrate models into scalable production workflows., Education: A Master's or PhD in a quantitative or technical field such as computer science, statistics, mathematics, engineering, or data science is required. Experience: 5-10 years of hands-on data science or machine learning engineering experience delivering models, pipelines, or analytics solutions is required. Technical Skills: * Strong Python development experience, including libraries such as pandas, NumPy, scikit-learn, or XGBoost. * Deep understanding of supervised machine learning classification approaches and model performance evaluation. * Experience with large structured datasets, data cleansing, transformation, and building data preparation workflows. * Experience packaging, versioning, testing, and integrating machine learning models into production application workflows. * Strong technical communication and documentation skills. * Experience using generative AI tools and applying prompt engineering techniques., * Experience developing classification models for fraud, risk, cybersecurity, or other high-volume decisioning use cases. * Familiarity with MLOps practices, including model packaging, version control, CI/CD pipelines, and model monitoring. * Experience designing or supporting real-time or near-real-time machine learning models. * Experience with distributed data processing or cloud platforms such as Spark, Databricks, AWS, or Azure. * Working knowledge of SQL for data extraction and validation. * Experience creating dashboards or visualizations to communicate model performance. * Understanding of explainable AI, model governance, and responsible AI practices. * Prior experience working in Agile delivery environments. ## Description * Design, build, train, validate, and tune supervised machine learning classification models using structured, behavioral, and transactional data. * Perform hands-on data exploration, cleansing, transformation, and feature engineering to create reliable inputs for model development. * Develop repeatable Python-based workflows, scripts, and pipelines for dataset preparation, model training, evaluation, and scoring. * Evaluate model performance using appropriate classification metrics, analyze false positives and false negatives, and iterate on model quality. * Package and deploy machine learning models into scalable production or near-production environments in partnership with engineering teams. * Troubleshoot data quality, pipeline, model performance, and integration issues across all phases of development and deployment. * Create clear technical documentation covering data assumptions, feature logic, model design, validation results, and implementation details. * Collaborate with engineers, analysts, and stakeholders to ensure delivered models are technically sound, maintainable, and aligned to measurable outcomes. ## Related Videos - [Vectorize all the things! 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