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Machine Learning / AI Engineer
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Kelly Mitchell
Sandy Springs, GA (In Person)
Full-Time
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Job Description
Job Summary Our client is seeking a Machine Learning / AI Engineer to design, build, and operate production-grade ML systems that drive real business impact with our client. In this role, you will own the full model lifecycle—from problem framing and data engineering through training, deployment, and ongoing monitoring—while collaborating closely with product, data engineering, and software teams. You will be expected to communicate complex trade-offs clearly to both technical and non-technical stakeholders and champion responsible AI practices across the organization. Core 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 classification, regression, ranking, clustering, anomaly detection, and NLP approaches Perform feature engineering, dataset creation, labeling strategies, and model evaluation with scientific rigor Implement model interpretability, bias assessment, and responsible AI techniques 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 Integrate ML models into applications through APIs, event streams, or workflow orchestration Document architecture, operational runbooks, and model cards; participate in design reviews and knowledge sharing Required Skills/Experience (Must-Haves) 4+ years building and integrating APIs (FastAPI / Flask) and 3rd-party API integrations 3+ years (Mid) / 5+ years (Senior) shipping ML systems into production—not just notebooks Strong Python skills and hands-on experience with ML libraries: scikit-learn, PyTorch, TensorFlow, XGBoost Data processing and analytics with Pandas, NumPy, SQL, and Spark Solid ML fundamentals: bias/variance trade-offs, evaluation metrics, cross-validation, feature engineering, error analysis Containerization with Docker and CI/CD pipeline management Version control with Git / GitHub / GitLab Strong SQL skills (SQL Server or Oracle) Scripting in Python, Bash, and/or PowerShell Ability to communicate trade-offs and results clearly to both technical and non-technical stakeholders Experience documenting architecture, model cards, and operational runbooks Cross-functional collaboration in Agile / DevOps environments Preferred Skills/Experience (Nice-to-Haves) Experience with workflow automation tools and orchestration platforms Background in highly regulated or financial services environments Familiarity with cloud ML platforms (AWS SageMaker, Azure ML, GCP Vertex AI) Knowledge of Disaster Recovery / Business Continuity Planning processes