We're looking for an AI Explainability Engineer Intern to join our Leidos team!The candidate helps to ensure that our AI systems are not both effective and interpretable. You will work with data scientists, machine learning engineers, and other stakeholders to develop AI tools. Your work assists in making the AI understandable to a large group of audiences. The candidate designs and implements explainability techniques by researching new methods and collaborating with cross-functional teams to integrate AI. The role requires an understanding of AI and machine learning principles and excellent problem-solving and communication skills. The AI solutions will be deployed on low-size, weight, and power endpoints. Therefore, the candidate must be familiar with maximizing capabilities when resources are limited.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 ResponsibilitiesExperiment with and test different explainability models for efficiency and understanding.Implement explainability models into AI solutions.Understand capabilities like SHAP and LIME.Familiarity with options for edge computing.Solve complex problems with multilayered data sets, and optimize existing machine learning libraries and frameworks Collaborate with data scientists, administrators, data analysts, data engineers, and data architects on production systems and applications Identify differences in data distribution that could potentially affect model performance in real-world applicationsEnsure algorithms generate accurate user recommendationsStay up to date with developments in the machine learning industryDevelop, 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 techniquesEngineer prompts for LLMs and Generative AIUse algorithms to identify complex patterns across multiple modalitiesIncrease the efficiency and quality of data alignment and fusionEnhance and maintain analysis tools, including automation of current processes using AI/ML algorithms quantitative data analysis, including developing retrieval, processing, fusion, analysis, and visualization of various datasetsConfigure and program prototypes Jupyter notebooks with ML solutionsSetup and use AWS instances to train and operate AI/ML modelsBasic QualificationsCollege students actively seeking a B.S. degree in Aerospace Engineering, Computer Science, Mathematics, Statistics, Physics, Electrical Engineering, Computer Engineering, or related fieldsMust be able to obtain a Top-Secret security clearance with a polygraph security clearanceUS citizenship requiredKnowledge of Deep Learning Frameworks such as Keras, Tensorflow, PyTorch, Mxnet, etc. - Ability to apply these frameworks to real problems in the 'time --series' domainAssist in the development and testing of commercial web applicationsCollaborate with senior developers on various software development projectsApply user-centered design principles in web application developmentParticipate in agile development processes and team meetingsContribute to the improvement of existing software and the creation of new featuresIntermediate software development skills lifecycle including developing and maintaining good production quality codeHands-on Software Development Skills (Python-Preferred)Experience or educational courses/projects in Machine Learning and TextPreferred QualificationsVisualizations/Web Development Skills (e.g., Tableau, D3).Hands-on experience with prototype developmentHands-on experience with automating data cleansing, formatting, staging, and transforming data humanHands-on experience applying data analyticsHands-on experience with prompt engineeringHands-on experience with reinforcement learningHands-on experience with LLMs and Generative AIHands-on experience with intelligent systems and machine learningExperience with the interpretability of deep learning modelsBig Data Skills (Azure, Hadoop, Spark, recent deep learning platforms)Experience with text mining tools and techniques, including in areas of summarization, search (e.g., ELK Stack), entity extraction, training set generation (e.g., Snorkel), and anomaly detectionHands-on experience with DKubeHands-on experience with KubeFlowHands-on experience with FeastOriginal Posting Date:2024-10-22While subject to change based on business needs, Leidos reasonably anticipates that this job requisition will remain open for at least 3 days with an anticipated close date of no earlier than 3 days after the original posting date as listed above.Pay Range:Pay Range $44,850.00 - $81,075.00The Leidos pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law.
