AI/ML Engineer - Data Science & MLOps

Job Title: AI/ML Engineer - Data Science & MLOps

Location: Atlanta, GA

Job Summary:

We are looking for an experienced AI/ML Engineer with a strong Data Science background to design, develop, deploy, and manage machine learning solutions across the complete ML lifecycle.

The ideal candidate should have hands-on experience in machine learning, statistical modeling, feature engineering, model development, deployment, monitoring, and optimization.

The role requires strong experience with MLOps practices to operationalize ML models at scale, including automated training and deployment pipelines, model versioning, experiment tracking, model monitoring, drift detection, and CI/CD.

The engineer will work closely with Data Scientists, Data Engineers, Cloud/DevOps teams, architects, and product stakeholders to deliver scalable and reliable AI/ML solutions.

Required Skills & Experience

Data Science & Machine Learning Strong foundation in Data Science, Machine Learning, statistics, and predictive modeling.

Hands-on experience with supervised and unsupervised ML techniques.

Strong understanding of feature engineering, model selection, hyperparameter tuning, validation, and performance evaluation.

Experience with ML frameworks such as: Scikit-learn TensorFlow PyTorch XGBoost Strong proficiency in Python and familiarity with SQL.

ML Lifecycle Management Demonstrated experience managing ML solutions from experimentation through production.

Experience with: Experiment tracking Model registry and versioning Dataset/model lineage Reproducibility Model deployment Model performance monitoring Data/model drift detection Automated retraining MLOps Strong hands-on experience with MLOps principles, tools, and practices.

Experience with one or more platforms/tools such as: MLflow Kubeflow Azure Machine Learning AWS SageMaker Google Vertex AI Databricks Experience building automated ML pipelines and integrating them with CI/CD workflows.

Experience with Docker, Kubernetes, Git, and CI/CD platforms such as Azure DevOps, GitHub Actions, Jenkins, or similar tools.

The emphasis on MLflow/Kubeflow and cloud ML platforms is consistent with internal examples.

One referenced MLOps profile specifically demonstrates end-to-end lifecycle automation using Kubeflow and Vertex AI, MLflow-based experiment/model tracking and registration, and Evidently-based model/data drift detection.

Cloud & Data Engineering Experience with at least one major cloud platform: Azure, AWS, or GCP.

Understanding of data engineering concepts, ETL/ELT pipelines, and data quality.

Experience working with SQL/NoSQL databases and large datasets.

Familiarity with scalable data processing and cloud storage technologies.

Preferred / Nice-to-Have Skills

Experience with Generative AI, LLMs, RAG, NLP, or conversational AI.

Exposure to LLMOps and GenAI lifecycle management. Experience with Databricks and/or Spark.

Knowledge of feature stores and data/model versioning tools.

Familiarity with model governance, explainability, responsible AI, and AI security.

Experience developing AI/ML applications in the healthcare domain.

Familiarity with Agile/Scrum delivery models.

This GenAI extension fits your earlier internal requirement for Python/Data Science/GenAI expertise, which covered model development, fine-tuning, evaluation, data pipelines, feature engineering, and end-to-end AI solution development.