Machine Learning Engineer

• 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.

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.

Everforth Apex is a world-class IT services company that serves thousands of clients across the globe. When you join Everforth Apex, you become part of a team that values innovation, collaboration, and continuous learning. We offer quality career resources, training, certifications, development opportunities, and a comprehensive benefits package. Our commitment to excellence is reflected in many awards, including ClearlyRateds Best of Staffing in Talent Satisfaction in the United States and Great Place to Work in the United Kingdom and Mexico.

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