Full Stack Software Engineer - R134

Location: Herndon, VA

Department: Computer Vision - Deep Learning/AI

Location Type: IN_OFFICE

Employment Type: FULL_TIME

• Design and develop end-to-end features spanning front-end interfaces and backend services 
• Build and maintain responsive, user-friendly web applications using modern JavaScript frameworks 
• Develop scalable backend systems and APIs using Python and cloud-native architectures 
• Integrate frontend components with backend services via RESTful APIs and streaming/data services 
• Deploy and manage applications in AWS environments using Docker and Kubernetes 
• Collaborate in an Agile development environment, contributing to sprint planning, design discussions, and continuous improvement 
• Work directly with end users to understand mission needs and translate them into technical solutions 
• Ensure performance, scalability, security, and reliability across the stack 
• Actively collaborate with teammates in design discussions and pair programming 
• Write clean, maintainable code with strong test coverage (unit, integration, and end-to-end) 
• Participate in code reviews, establish best practices, and contribute to engineering standards 

• U.S. Citizenship 
• Active TS/SCI clearance 
• 5+ years of professional software engineering experience across front-end, back-end, or full stack development 
• Experience building applications in AWS cloud environments 
• Proficiency in:
  • Frontend: React, Angular, Vue, or similar (TypeScript preferred) 
  • Backend: Python (preferred) or other object-oriented languages 
• Experience with REST APIs, microservices, and distributed systems 
• Familiarity with Docker, Kubernetes, or other containerized environments 
• Experience with relational databases and query optimization 
• Strong understanding of software development best practices and SDLC 
• Experience with Git and branching strategies 
• Comfortable working in Linux-based environments 

• Experience with modern frontend tools (e.g., TailwindCSS, React Query, Vite, MUI) 
• Familiarity with GraphQL and real-time data applications 
• Experience with AWS services (e.g., Lambda, RDS, DynamoDB, S3, IAM, EC2, SQS) 
• Knowledge of performance optimization techniques (frontend or backend) 
• Exposure to AI/ML systems or data pipelines at scale 
• Experience contributing to technical leadership, mentoring, or architecture decisions