AI Web Application Developer Intern

We're looking for an AI Web Application Developer Intern to join our Leidos team!

The Intern will assist in the development and maintenance of AI-driven web application monitoring tools. Collaborate with the AI and development teams to identify and resolve performance issues using machine learning techniques. Implement AI-based monitoring solutions to ensure the reliability and availability of web applications.

The position requires general machine learning and artificial intelligence knowledge so that the candidate can contribute to various machine learning (ML) and artificial intelligence (AI) projects in the Leidos AI/ML Accelerator. Candidates must be familiar with handling multiple types of data (Text, image, video, audio, lidar, etc..), primarily related to large language models (LLMs)/Generative AI and using transformers. The candidates must understand the theoretics of reinforcement learning, enabling continuous learning and task optimization. Ethical AI is part of all Leidos AI/ML projects. Consequently, we want candidates to know the concepts of Ethical AI and how to apply them.

Along with those skills, the candidate must have demonstrated the ability to work independently and in technical teams to implement and customize algorithms to fuse multiple data modalities. In this position at Leidos Arlington, VA. the candidate should have at least intermediate Python coder ability and hands-on experience using ML libraries like SciKit Learn, DKube, KubeFlow, Feast, Azure, TensorFlow, Keras, etc. The candidates' knowledge should also include experience containerizing AI models and using the containers with AWS, Microsoft Azure, or Google Cloud.

Primary Responsibilities

• Build reusable, testable, and efficient code for front-end and back-end systems.
• Develop, deploy, and manage AI/ML models, including Generative AI models (GPT, DALL-E, etc.), to solve business problems.
• Work with data scientists to integrate AI/ML models into production environments.
• Fine-tune models, manage version control and monitor performance in production systems.
• Develop and maintain CI/CD pipelines to automate model deployment and web application releases.
•  Implement DevOps best practices for infrastructure as code (IaC) using tools like Docker, Kubernetes, and Terraform.
• Conduct anomaly detection using various AI/ML techniques
• Properly configure general adversarial networks on their own and in conjunction with training such as Generative AI
• Understanding of autoencoders and their parameters
• Engineer prompts for LLMs and Generative AI
• Use algorithms to identify complex patterns across multiple modalities
• Increase the efficiency and quality of data alignment and fusion
• Design rewards for reward model training in reinforcement learning
• Enhance and maintain analysis tools, including automation of current processes using AI/ML algorithms quantitative data …