Machine Learning Engineer/AI Engineer

Introduction

This role involves developing and deploying machine learning models and AI systems in a hybrid work environment in Atlanta, GA. The position is a 6-month contract with a salary range of $70-$75.00 per hour on W2.

Required Skills & Qualifications

• 3 years (Mid) / 5 years (Senior) experience shipping ML systems into production (not just notebooks).
• Strong programming skills in Python and familiarity with ML libraries (e.g., scikit-learn, PyTorch, TensorFlow, XGBoost).
• Experience with data processing and analytics using tools such as Pandas, NumPy, SQL, Spark (as applicable).
• Solid understanding of ML fundamentals: bias/variance, evaluation metrics, cross-validation, feature engineering, and error analysis.
• Experience building and deploying ML services (e.g., FastAPI/Flask), containerization (Docker), and CI/CD.
• Ability to communicate tradeoffs and results clearly to both technical and non-technical stakeholders.
• 4 years of experience developing APIs and integrating with 3rd party APIs.
• Strong SQL skills (SQL Server or Oracle).
• Familiarity with scripting languages (Python, Bash, Powershell).
• Experience with version control systems (Git, GitHub, GitLab).
• Knowledge of CI/CD pipelines and DevOps best practices.
• Understanding of workflow automations is a plus.
• Excellent problem-solving, analytical, and communication skills.

Preferred Skills & Qualifications

• Prior work experience at client or in client's industry.

Day-to-Day Responsibilities

• Partner with stakeholders to frame business problems as ML/AI use cases, define success metrics, and identify required data.
• Build and iterate on models using appropriate approaches (e.g., classification, regression, ranking, clustering, anomaly detection, NLP).
• Perform feature engineering, dataset creation, labeling strategies, and model evaluation with strong scientific rigor.
• Implement techniques for model interpretability, bias assessment, and responsible AI where applicable.
• Build production-grade ML services and pipelines (batch real-time), ensuring performance, reliability, and maintainability.
• Deploy models to cloud environments using CI/CD and infrastructure-as-code best practices.
• Implement monitoring for data drift, model drift, latency, throughput, cost, and model quality.
• Maintain versioning for datasets, features, models, and experiments to ensure repeatability and governance.
• Collaborate with data engineering to create robust data pipelines and ensure data quality.
• Work with software engineers to integrate ML into applications through APIs, event streams, or workflow orchestration.
• Document architecture, operational runbooks, and model cards; participate in reviews and knowledge sharing.

Company Benefits & Culture

• Competitive pay and flexible working arrangements.
• Opportunities for professional development and career advancement.
• Collaborative and innovative work environment.

For immediate consideration please click APPLY to begin the screening process with Alex.