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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in building and deploying LLM-powered applications and agentic workflows, with a strong focus on MLOps, CI/CD practices, and infrastructure management. Proficient in Python and Kubernetes, with a solid understanding of data handling, access control, and observability in AI environments.
Highest-signal resume keywords
LLM Application DevelopmentMLOps ExperienceKubernetes DeploymentCI/CD ImplementationData Handling Practices
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
PythonLLM APIsRAG SystemsEmbeddingVector DatabasesAnsibleInfrastructure-as-CodeModel ServingPrompt EngineeringQuality Measurement
Soft Skills
Quick LearnerCross-Functional Collaboration
Tools & Technologies
KubernetesCI/CD PipelinesMonitoring ToolsLogging ToolsIdentity Systems
Certifications & Qualifications
Security Clearance
Industry Keywords
AI InfrastructureData Boundary ConcernsAgent DesignObservability for AI/MLRegulated Environments
Tech Stack
Tools & technologiesAnsibleKubernetesPython
About the role
Key responsibilities & impact- Design and build LLM-powered tools and agentic workflows that automate work and improve productivity
- Extend and improve RAG systems, including ingestion, chunking, embedding, retrieval, ranking, and evaluation
- Structure retrieval around the organization’s information hierarchy while improving relevance and access boundaries
- Build tool integrations connecting LLMs to internal systems and data sources
- Design safe agents with guardrails, human-in-the-loop processes, and clear failure behavior
- Establish evaluation and testing frameworks to measure quality and catch regressions
- Partner with teams across the company to identify high-value use cases and deploy tools
- Deploy AI tools and services from prototype to reliable production
- Build and operate Kubernetes infrastructure hosting models, tools, and supporting services
- Manage model serving, inference endpoints, APIs, and gateways
- Implement monitoring, logging, and usage observability
- Ensure retrieval and agent tools respect access boundaries and prevent data leakage
- Integrate identity and permission systems so tools honor access rights
- Apply defense-appropriate data-handling practices
- Build CI/CD pipelines for AI tools, services, and agents
- Automate provisioning and configuration with Ansible and infrastructure-as-code
- Build data pipelines to ingest, transform, and index content for RAG and AI applications
- Manage vector databases and other stores supporting retrieval and AI workloads
- Maintain reproducible development, staging, and production environments
Requirements
What you’ll need- Bachelor's in Computer Science, Software Engineering, Data Engineering, or related field; equivalent industry experience also welcome
- 3–6+ years of experience in MLOps, software, platform, or backend engineering
- Strong proficiency in Python and comfort building, shipping, and operating services
- Experience building LLM-powered applications, including LLM APIs or self-hosted models, prompts, and tool/function calling
- Hands-on experience with Kubernetes and containerized deployment
- Solid understanding of CI/CD, infrastructure-as-code, and production service reliability
- Awareness of access control and data-boundary concerns when connecting tools to sensitive internal systems
- Demonstrated ability to learn quickly and work across unfamiliar parts of the stack
- Depth in at least one core area: LLM application development, RAG/retrieval, agent design, or AI infrastructure, with interest in growing into the others
- Ability to obtain or maintain a security clearance
- Preferred: hands-on experience with RAG systems, embeddings, and vector databases
- Preferred: experience designing and shipping agentic workflows, including tool use, orchestration, and guardrails
- Preferred: familiarity with Model Context Protocol (MCP) or similar tool-integration frameworks
- Preferred: experience integrating LLM tools with enterprise systems via APIs
- Preferred: knowledge of LLM evaluation, prompt engineering, and quality/regression measurement
- Preferred: experience serving models and optimizing inference with vLLM, TGI, Triton, or similar
- Preferred: familiarity with LangChain, LlamaIndex, or equivalent
- Preferred: experience with Ansible for configuration management and automation
- Preferred: experience implementing identity, authentication, and fine-grained authorization, including OAuth, SSO, and RBAC
- Preferred: observability experience for AI/ML workloads
- Preferred: GPU infrastructure and scheduling experience
- Preferred: understanding of security and data-handling requirements in regulated or defense environments
Benefits
Comp & perks- Competitive total compensation package
- Comprehensive benefit package options including medical, dental, vision, life, and more
- 401k with company match
- 3 weeks of paid time off each year
- 40 hours of sick time
- 12 annual company holidays
- Professional development and career advancement opportunities
- Innovative environment focused on cutting-edge defense and aerospace technology
- Collaborative culture