Senior Gen AI Developer

KBR, Inc.•El Segundo, CA

1d•Remote

About The Position

KBR’s National Security Solutions team provides high-end engineering and advanced technology solutions to our customers in the intelligence and national security communities. In this position, your work will have a profound impact on the country’s most critical role – protecting our national security. KBR is seeing a Senior Gen AI Developer to join our team in El Segundo, CA (remote). The MILSATCOM Systems Engineering, Integration, and Test (MSEIT) effort provides leading edge Systems Engineering & Integration (SE&I) for the US Space Force’s Space Systems Center (SSC). We support the Space Force’s acquisition of state-of-the-art satellite communications systems, providing global secure, survivable, and protected communications for our nation’s warfighters. We seek technical individuals who will thrive in a highly collaborative work environment of small teams, using the most modern tools and methodologies to tackle the challenges of integrating complex space and ground communications systems.

Requirements

Ability to obtain and maintain a Secret security clearance
Bachelor’s degree in Computer Science, Software Engineering, Artificial Intelligence, or a related technical discipline
15+ years of software development experience
3+ years building generative AI or LLM-powered applications
Strong proficiency in Python for AI/ML development, data processing, and automation
Hands-on experience designing, developing, and deploying applications using large language models (LLMs)
Proven experience building and optimizing Retrieval-Augmented Generation (RAG) pipelines using embeddings, vector databases, and semantic search
Experience with prompt engineering, LLM orchestration, and developing agent-based workflows
Familiarity with LLM frameworks such as LangChain, LlamaIndex, or Semantic Kernel
Experience integrating AI solutions with enterprise data sources, APIs, and knowledge graphs
Ability to evaluate and fine-tune model outputs for quality, bias, explainability, and performance
Experience deploying and maintaining applications in cloud-native environments (Azure, Kubernetes, CI/CD pipelines)
Solid understanding of software development lifecycle (SDLC) and DevOps practices for production AI systems

Nice To Haves

Master’s in Computer Science, Artificial Intelligence, or a related field
Experience integrating knowledge graphs and graph databases (e.g., Neo4j, ArangoDB) into LLM-driven applications
Exposure to multi-modal AI models (text, image, audio) and their application in real-world systems
Experience building custom LLM evaluation pipelines and benchmarking frameworks
Familiarity with model optimization techniques or edge deployment for constrained environments
Experience deploying AI solutions in defense, government, or other high-security environments
Knowledge of responsible AI practices, including bias mitigation, explainability, and governance

Responsibilities

Designing, developing, and deploying applications using large language models (LLMs)
Building and optimizing Retrieval-Augmented Generation (RAG) pipelines using embeddings, vector databases, and semantic search
Prompt engineering, LLM orchestration, and developing agent-based workflows
Integrating AI solutions with enterprise data sources, APIs, and knowledge graphs
Evaluating and fine-tuning model outputs for quality, bias, explainability, and performance
Deploying and maintaining applications in cloud-native environments (Azure, Kubernetes, CI/CD pipelines)
Applying software development lifecycle (SDLC) and DevOps practices for production AI systems