Job Description

AI Specialist

Location: Charlotte, NC
Type: Contract or Full-Time
Compensation:

About the Role

We are seeking a highly skilled Machine Learning Engineer / AI Specialist to join a dynamic and fast-evolving data science team. The ideal candidate will bring strong technical expertise in AWS SageMaker, Python programming, and MLOps practices, along with a deep understanding of scalable model development and deployment in production environments.

This role is ideal for someone who thrives at the intersection of data science, engineering, and automation—building, optimizing, and maintaining robust machine learning systems that drive real business impact.

Key Responsibilities

• Design, develop, and deploy machine learning models using AWS SageMaker.

• Build and maintain ML pipelines for model training, validation, and deployment.

• Implement MLOps best practices, including CI/CD workflows for model lifecycle automation.

• Collaborate closely with data scientists to productionize research models.

• Monitor and optimize model performance, cost, and reliability; implement automated retraining processes.

• Develop and maintain model versioning, experiment tracking, and data validation frameworks.

• Debug and maintain Terraform and Concourse pipelines; proactively update based on organizational changes.

• Migrate repositories to GitHub and update associated pipelines for continuous integration.

• Ensure data quality, governance, and reproducibility of model outputs.

• Participate in code reviews, maintain clean, modular code, and create detailed technical documentation.

Required Qualifications

• Bachelor’s degree in Computer Science, Data Science, Engineering, or related field (or 8+ years equivalent experience).

• 3+ years of experience in machine learning engineering, AI development, or data science operations.

• Strong Python programming skills; proficiency in NumPy, Pandas, Scikit-learn, and related libraries.

• Hands-on experience with AWS SageMaker for training, tuning, and deploying models.

• Solid background in data science methodologies and statistical analysis.

• Experience with Infrastructure-as-Code tools (Terraform, CloudFormation).

• Deep understanding of MLOps, containerization (Docker, Kubernetes), and CI/CD pipelines.

• Familiarity with GitHub Actions, version control, and collaborative development workflows.

• Working knowledge of AWS services (S3, EC2, Lambda, CloudWatch).

Preferred Qualifications

• Master’s degree in a relevant technical field.

• AWS Certifications (e.g., Machine Learning Specialty, Solutions Architect).

• Experience with monitoring tools (Prometheus, Grafana, CloudWatch) and big data frameworks (EMR, Spark, Hadoop).

• Strong SQL expertise (CTEs, indexes, stored procedures, and performance optimization).

• Experience with ETL tools (SSIS, Sqoop, Spark).

• Hands-on experience building classification and regression models.

• Familiarity with software engineering best practices and design patterns.