DevOps/AI Engineer

Roles & Responsibilities

• Expanding prototyping and proof of concepts while advancing systems engineering practices to improve sponsor outcomes.
• Increasing the use of modeling throughout the SELC and utilizing modeling to advance the implementation of Systems of Systems.
• Leveraging AI and ML (including LLMs) and emerging technologies to improve systems methodology and accelerate speed of delivery for our sponsors.
• Developing reusable artifacts and prototypes.
• Improving system interoperability with a focus on System of Systems and enhancing data management practices and solutions for more efficient data management.
• Modernizing legacy IT infrastructure; implementing secure, scalable, and efficient IT solutions; accelerating adoption of cloud and emerging IT technologies; and promoting automation to enhance operational efficiency and delivery for sponsors.

Basic Qualifications

• Bachelor’s degree in Computer Science, Computer Engineering, Systems Engineering, or a related technical discipline (e.g., Electrical Engineering, Data Science, or Applied Mathematics)
• 0–2 years of relevant experience in hands on software development, systems, and/or DevOps, including internships, co-ops, or full-time roles
• PRACTICAL understanding of the software development lifecycle, with experience using modern programming languages such as Python, Java, or C++
• FAMILIARITY with DevOps concepts such as automation, testing, CI/CD pipelines, or containerization (e.g., Docker, GitHub Actions, or Jenkins)
• FONDATIONAL understanding of cloud platforms (e.g., AWS, Azure, or GCP) and how they support scalable, distributed systems
• EXPOSURE to Agile or Scrum methodologies and collaborative software development practices
• STRONG analytical, communication, and teamwork skills, with demonstrated ability to learn new tools and technologies quickly
• Able to MAINTAIN and OBTAIN a SECRET clearance

Preferred Qualifications

• 1–2 years of experience or strong project background applying DevOps, MLOps, or AI/ML techniques to real-world or simulated systems
• FAMILIARITY with cloud-native technologies or infrastructure as code (e.g., AWS, Azure, or GCP), including provisioning IaaS/SaaS and automating deployments to enhance operational efficiency
• EXPOSURE to large language models (LLMs) and emerging AI tools (e.g., OpenAI, Hugging Face, LangChain) and interest in applying them to engineering workflows or digital transformation efforts
• EXPERIENCE in software development process, tools and languages including Java, Python, SQL, JavaScript, XML, JSON, HTML/CSS, Shell, MySQL, MongoDB and Git
• EXPERIENCE developing or deploying applications using modern frameworks, APIs, or microservices architectures
• KNOWLEDGE of data management principles, database design, and approaches for working with structured and unstructured data
• EXPERIENCE with prototyping and promoting reuse of artifacts and prototypes