An Artificial Intelligence Engineer develops, tests, and deploys artificial intelligence models. May work closely with data software engineers and data professionals to train and implement AI models into existing systems or develop new applications.
The essential functions of the job include, but are not limited to, the following: Utilize and expand AWS Cloud infrastructure to enhance data analytics and monitoring capabilities (AWS full stack development, EC2, S3, Lambda, Amplify) Utilize AI models for knowledge base agent/digital twin of gas turbine. Develop/maintain software for monitoring, analyzing and graphically representing gas turbine operations. Continuously refine/train models to mimic real life engine behavior, minimize false alarms, and predict gas turbine alarms before they occur Work with operational team to support and troubleshoot issue with customer data and cloud infrastructure Continuously improve the OSIsoft PI system architecture Other duties as assigned
MINIMUM REQUIREMENTS
Education:
Bachelor's degree in Computer Science, Data Science, Data Analytics , Mechanical Engineering, or a related quantitative/technical field .
Generative AI & ML:
Practical experience with Machine Learning (ML) and Artificial Intelligence (AI) frameworks. Familiarity with Large Language Models (LLMs) and Foundational Models via platforms like Amazon Bedrock.
Full-Stack Development:
Understanding of AWS Full-Stack development workflows, including API Management, IAM roles, and cloud security best practices.
AWS Cloud Ecosystem:
Hands-on experience with AWS Cloud Architecture. Proficiency in core services including EC2 (Compute), S3 (Storage), and AWS Lambda (Serverless).
Programming Languages:
Proficiency in at least one modern language: Python, TypeScript, C#, or MATLAB.
DevOps & Tools:
Basic knowledge of Software Development Life Cycle (SDLC), Version Control (Git/GitHub), and Continuous Integration/Continuous Deployment (CI/CD) pipelines.
Methodology:
Familiarity with Agile/Scrum environments and iterative development processes.
Data Analysis:
Strong aptitude for data visualization and data modeling.
DataBase experience:
Experience with SQL and noSQL databases.
Preferred Skills/Experience AWS Certifications:
Active certifications such as AWS Certified Developer, Solutions Architect, or Machine Learning - Specialty.
Advanced AI/ML:
Exposure to Natural Language Processing (NLP), Neural Networks, or Generative AI integration via Amazon Bedrock and SageMaker.
Data Science Stack:
Proficiency in data manipulation libraries (e.g., Pandas, NumPy, Scikit-learn)
Modern Web Frameworks:
Experience in Full-Stack Development using React.js and TypeScript to build responsive user interfaces.
API Development:
Knowledge of building and consuming RESTful APIs or GraphQL to connect front-end applications with cloud backends.
State Management:
Familiarity with tools like Redux, Context API, or Zustand for complex UI logic.
Version Control:
Mastery of Git workflows, including branching strategies, pull requests, and code reviews.
CI/CD Pipelines:
Experience with AWS CodeCommit, GitHub Actions, or GitLab CI for automated testing and deployment. Infrastructure as Code (IaC ): Basic understanding of AWS CloudFormation or Terraform is a significant advantage. Equal Opportunity Veterans/Disabled
