About
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.
Languages
- English
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