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AI Engineer Intern

LMI
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Overview

LMI is a consultancy dedicated to powering a future-ready, high-performing government, drawing from expertise in digital and analytic solutions, logistics, and management advisory services. We deliver integrated capabilities that incorporate emerging technologies and are tailored to customers’ unique mission needs, backed by objective research and data analysis. Founded in 1961 to help the Department of Defense resolve complex logistics management challenges, LMI continues to enable growth and transformation, enhance operational readiness and resiliency, and ensure mission success for federal civilian and defense agencies.
 
The AI Engineer Intern will work with Forge, LMI’s internal technology accelerator and innovation hub, to develop technology solutions for LMI and its federal government clients. This position will apply machine learning and data science approaches to initiate, develop, and deliver AI within web-based applications. The AI Engineer Intern will work within a team of data scientists, computer scientists, application developers, product managers, and designers to integrate AI/ML into applications to automate and improve business processes, data quality, and user experience. The ideal candidate will have a passion for solving challenging problems by applying a diverse set of technical capabilities to program, develop models, and deploy services consumed by user applications.

Responsibilities

  • Identify opportunities where AI/ML or modeling and simulation can generate business insights or improve business processes
  • Develop and implement digital and analytic approaches
  • Work with product designers, application developers, infrastructure engineers, and other data scientists to integrate predictive and prescriptive models with web-based applications
  • Research algorithms in machine learning to identify viable approaches to meet business requirements
  • Apply machine learning methods, such as natural language processing (NLP), computer vision, regression, clustering, classification, and deep learning
  • Design solution prototypes that connect to data sources and deploy through services, such as service functions in web applications or application programming interfaces (APIs)
  • Become familiar with DevSecOps principles to continuously deliver high-quality software
  • Work with Docker to develop and deploy containerized versions of models
  • Participate in design and code reviews, and collaborate with a strong, passionate engineering team
  • Provide input to UX/UI designers and front-end application developers on how to effectively deliver model outcomes to users
  • Interface with customer stakeholders to provide technical explanations and support

Qualifications

  • Bachelor’s degree in engineering, mathematics, computer science, or related technical discipline
  • Currently enrolled in a graduate program
  • Pursuing a post-graduate degree in engineering, mathematics, computer science, modeling and simulation, operations research, or related technical discipline
  • Must be able to work for a minimum of 10 weeks beginning in May or June of 2021
  • Comfortable working with agile teams, developing prototypes and functionality in short development sprints
  • Ability to work independently and collaborate effectively with a project team in an agile research and development environment
  • Comfortable using Atlassian products, including JIRA, Bamboo, Confluence, FishEye, Crucible, and Bitbucket
  • Ability to think critically to propose tractable solutions to complex problems
  • Effective written and verbal communication skills
  • Ability to communicate complex concepts to both technical and business-focused audiences
  • Familiarity or desire to excel with modern programming languages appropriate for machine learning prototyping; Python is preferred, but experience with other languages such as Java, C++, C#, JavaScript and R demonstrate the necessary ability
  • Familiarity with the underlying mathematics of machine learning
  • Knowledge of data structures and data management principles, methods, and tools
  • Desire to explore machine learning frameworks and libraries, such as scikit-learn, TensorFlow, and Spark ML
  • Ability to work with integrated development environments, such as Jupyter, JupyterLab, JetBrains IntelliJ IDEA, JetBrains PyCharm, JetBrains CLion, and Visual Studio Code
  • Ability to collaborate with a team that develops applications using web development frameworks, such as Angular (1.x/2+), React, and Ember, and server frameworks, such as Node.js and Express.js
  • Ability to consume or interface with cloud computing and storage services, including Amazon Web Services (AWS), Microsoft Azure, or Google Cloud
  • Familiarity or desire to become familiar with containerization technologies, such as Docker, Kubernetes, Amazon Elastic Container Service (ECS), and Amazon Elastic Kubernetes Service (EKS)