Artificial Intelligence & Cloud Engineer

CACI International•Norfolk, VA

1d•$98,500 - $206,800•Onsite

About The Position

Join CACI as the prime contractor on a growing program supporting NAVSEA on the Navy Maintenance and Modernization Enterprise Solution (NMMES), a mission-critical program that supports over 45,000 users executing naval ship and submarine maintenance operations worldwide. CACI is seeking an Artificial Intelligence & Cloud Engineer to research, design, build, and deploy Artificial Intelligence (AI) and Machine Learning (ML) solutions. As part of our team, you will help lay the foundation for future AI/ML initiatives while contributing to broader software development projects. You will work closely with cross-functional teams to gather requirements, design, develop, and implement AI-driven features, seamless integrations, and deployment pipelines across a range of AI/ML use cases, including Generative AI. This role requires a hands-on engineer who understands how to operate effectively within dynamic, high-change enterprise environment. The ideal candidate brings deep technical expertise combined with real-world experience working in non-pristine, legacy-integrated ecosystems, where adaptability, pragmatism, and collaboration are essential.

Requirements

Active Secret Security Clearance (possesses or must be able to obtain).
Bachelor's degree in computer science, software engineering, or a related field.
2-5 years of relevant experience in NLP, machine learning, or AI development
Experience designing agentic flows using supervisor, routing, and hierarchical patterns with frameworks such as LangGraph or equivalent
Experience with Linux and Linux based terminals
Understanding of RAG architecture and principles, experience implementing RAG systems, and familiarity with popular RAG frameworks and tools.
Knowledge of security best practices for AI systems
Strong programming skills, particularly in Python, Javascript, rust, golang
Experience with vector databases, embedding techniques, and data pipelines
Experience with information retrieval systems
Experience with RESTful APIs and Cloud Services (AWS, OCI)
Experience with data preprocessing, normalization, cleaning, encoding data intvector representations
Knowledge of text analytics and semantic search techniques
Experience with prompt engineering
Experience working with multiple teams in an agile environment, with data scientists, UI/UX designers, and subject matter experts to improve the overall system.
Self-motivated with ability to quickly adapt and learn emerging disciplines and techniques

Nice To Haves

Experience supporting US Navy Cyber Risk Management Framework governed environments.
Familiarity with large-scale program environments like AWS GOV Cloud or Bluewater.
Background working alongside third-party vendors with limited enterprise execution capability.
Experience with hybrid architecture, legacy system integration, and modernization strategies.
Experience with DevSecOps, infrastructure as code

Responsibilities

Forward thinking vision of how RAG can be integrated within a software development lifecycle and business practices, keeping abreast of new developments in RAG, NLP, and related fields.
Designing and implementing retrieval systems: Creating efficient ways to store and quickly access large amounts of relevant information.
Implementing safeguards: Developing mechanisms to prevent the system from retrieving or generating inappropriate or harmful content.
Developing and fine-tuning language models: Working with large language models to optimize their performance for specific tasks and domains.
Evaluation and testing: Designing and conducting tests to measure the system's accuracy, relevance, and overall performance.
Integrating retrieval and generation components: Ensuring seamless interaction between the retrieval system and the language model.
Follow a CACI agile methodology, attending daily standups, refinement sessions, and updating agile project management system to ensure transparency
Design and implement agentic workflows using supervisor and orchestration patterns with frameworks such as LangGraph, CrewAI, or similar multi-agent orchestration tools
Establish observability and evaluation pipelines using tools like Langfuse, or similar platforms to monitor, trace, and assess LLM application performance