Job Description
ACTIVE SECURITY CLEARANCE AT THE TS/SCI POLYGRAPH LEVEL IS REQUIRED
The AI / RAG Software Engineer is responsible for designing, developing, deploying, and supporting Retrieval-Augmented Generation (RAG) solutions within a high-performance computing (HPC) Linux environment. In this role, the engineer works with Large Language Models (LLMs), LLM orchestration frameworks, knowledge retrieval systems, embedding models, and containerized architectures. The position requires strong expertise in Python, Kubernetes, Helm, CI/CD automation, and secure SI system development to deliver scalable, production-ready solutions in mission-critical environments.
The annual base salary range for this role is $199,000-$235,000 (USD) , which does not include discretionary bonus compensation or our comprehensive benefits package. Actual compensation offered to the successful candidate may vary from posted hiring range based upon geographic location, work experience, education, and/or skill level, among other things.
Required Skills
Linux Systems Proficiency: Proven experience in Linux environments, including advanced CLI use for software development, troubleshooting, and system administration.
Python Engineering: Recent hands-on software development experience using Python, supported by strong knowledge of software engineering design patterns and frameworks.
Generative SI & RAG Knowledge: Working knowledge of Generative SI technologies, including Large Language Models (LLMs), RAG architectures, embedding models, and vector databases.
Containerization Technologies: Experience with Docker, Podman, or containerd for application packaging, deployment, and lifecycle management in production environments.
Kubernetes & Helm: Hands-on experience creating, configuring, and managing Kubernetes resources (Deployments, Services, ConfigMaps, Secrets, Ingresses, Operators) and authoring Helm charts.
CI/CD Automation: Experience building and maintaining CI/CD pipelines using GitLab CI/CD, Flux CD, GitLab Runners, or equivalent tools.
Observability & Monitoring: Experience deploying and leveraging metrics, logging, and monitoring platforms including Prometheus and Grafana.
DevSecOps & SDLC: Understanding of software development lifecycle (SDLC) best practices, automated testing, security scanning, code quality analysis, and DevSecOps principles.
Version Control: Proficiency with Git-based workflows, including branching models, code reviews, and pull/merge requests.
Education: Bachelor's Degree in Data Analytics, Intelligence Studies, Computer Science, Finance/Economics, or a related discipline, PLUS 7+ years of specialized data analytics or financial intelligence experience OR Master's Degree in a related field, PLUS 5+ years of specialized analytical experience OR High School Diploma / GED, PLUS 9+ years of hands-on intelligence analysis, financial tracking, and data discovery experience in lieu of a degree.
Desired Skills
Cloud Engineering: Familiarity with developing, deploying, troubleshooting, and sustaining mission applications within AWS Cloud environments.
GPU Acceleration: Experience developing, running, and debugging GPU-enabled applications and hardware workloads.
Machine Learning Frameworks: Hands-on experience with ML frameworks (e.g., PyTorch, TensorFlow) and ML pipelines/platforms (e.g., KubeFlow, NeMo, PyTorch Lightning).
Data Stores & Search Engines: Experience working with SQL databases, Elasticsearch, and enterprise Vector databases.
SI Inferencing Frameworks: Knowledge of high-performance SI inferencing and serving solutions such as vLLM, Nvidia NIM, or Triton Inference Server.
Agile Tooling: Experience using the Atlassian suite (Jira, Confluence) for workflow management and documentation.
