AI Engineer
AI Engineer — Roles & Responsibilities
Locations – Malvern, Pennsylvania, and Charlotte NC United States
Responsibilities
- Design, develop, and deploy AI and Machine Learning solutions on AWS.
- Build scalable ML data pipelines using AWS Glue, Lambda, S3, Redshift, and Apache Spark.
- Develop data preprocessing and feature engineering pipelines using Python and PySpark.
- Build, train, evaluate, and deploy machine learning models using industry-standard ML frameworks.
- Integrate AI/ML models into enterprise applications and data platforms.
- Design scalable orchestration workflows using AWS Step Functions and Terraform.
- Develop APIs and services for AI model inference and deployment.
- Work with structured and unstructured datasets, including NoSQL databases.
- Optimize model performance, scalability, and inference latency.
- Build CI/CD pipelines for machine learning workflows and automate model deployment.
- Collaborate with Data Engineers, Data Scientists, Product Managers, and business stakeholders.
- Monitor model performance, retrain models, and ensure production reliability.
- Implement security, governance, and compliance for AI solutions.
- Utilize AI-assisted development tools such as Claude Code or equivalent to accelerate development and improve code quality.
- Create technical documentation and mentor junior engineers on AI best practices.
Required Skills
- AWS S3, Glue, Lambda, SQS, SNS, EventBridge, IAM
- AWS Redshift
- Python
- PySpark
- Apache Spark
- Machine Learning libraries (TensorFlow, PyTorch, Scikit-learn, etc.)
- AWS Step Functions
- Terraform
- NoSQL Databases (MongoDB, DynamoDB, Cassandra, etc.)
- Cloud Architecture on AWS
- CI/CD
- GitHub Actions
- Claude Code or equivalent AI coding tools
- Strong understanding of ML lifecycle, feature engineering, model deployment, and MLOps
These responsibilities closely align with the technical stack and expectations shown in the image and are suitable for senior Data Engineer and AI Engineer positions.