AI summary
Hands-on AI Developer role building and scaling production AI services, autonomous agents, and internal applications on a self-hosted, GPU-accelerated platform. RAVE Aerospace builds in-flight entertainment and connectivity solutions for airlines worldwide. Standout perk: discretionary bonus plus 401(k) match and a year-end holiday shutdown.
Key skills
PythonSQLLLM-based application developmentRetrieval pipelines (embeddings, vector search)Vector databases (Qdrant/pgvector/Weaviate/Pinecone)Managed LLM platforms (Bedrock/Anthropic/OpenAI/Azure OpenAI)SSO/OIDC and RBACReact (or similar)Docker/containerized services & networkingObservability and production operations
Salary not listed — comparable senior AI/LLM engineering roles in Brea/greater OC typically pay around $160k–$220k base, plus bonus/equity depending on level.
You'll thrive here if you can own end-to-end AI features (from prompt/RAG design to production rollout) and are genuinely fluent using AI coding agents daily in a collaborative, fast-moving environment.
Why apply
- Discretionary bonus program
- 401(k) with company match
- Year-end holiday shutdown
RAVE Aerospace is redefining the in-flight experience through innovative entertainment and connectivity solutions trusted by airlines around the world. We combine advanced hardware, intelligent software, and connected digital platforms to help airlines create more engaging, seamless, and reliable passenger experiences. As the future of air travel evolves, RAVE is building the technology that keeps passengers connected from takeoff to landing.
We are seeking an experienced AI Developer to design, build, and scale production AI applications and autonomous agents on a modern, self-hosted platform. This role will play a key part in shaping the architecture, tooling, and engineering standards that power AI capabilities across the organization. Working across the full technology stack, the AI Developer will leverage large language models and AI coding agents as core components of the development process to deliver reliable, high-impact solutions. This is a highly influential, hands-on engineering role focused on building practical, production-ready AI systems in a fast-moving and collaborative environment.
This individual is equally comfortable discussing architecture and implementing solutions, with a strong sense of ownership and a desire to influence platform direction, tooling decisions, and engineering best practices. They are curious, adaptable, and energized by solving complex problems while building scalable, maintainable systems. Strong collaboration and communication skills are essential, as this role partners closely with technical and cross-functional teams to bring AI capabilities into production.
Design and implement AI-powered services, agents, and internal applications
Develop end-to-end features — backend services, data pipelines, and user-facing UIs — using agent-assisted workflows
Design retrieval pipelines (chunking, embeddings, vector search) and prompt strategies
Enforce shared platform conventions: authentication, RBAC, container networking, secrets handling
Mentor other engineers on applied AI patterns and on getting the most out of agentic coding tools
10+ years of experience in software engineering
2+ years working directly with AI/ML or LLM-based applications
Strong software engineering fundamentals) — systems thinking, debugging, API design, data modeling
Production experience shipping web services and/or data products in any modern stack
Demonstrated fluency with AI coding agents (Claude Code, Cursor, Copilot, Aider, or similar) as part of daily work
Solid SQL and relational data modeling skills
A pragmatic approach to tests, observability, and operational quality
Model Context Protocol (MCP) — building servers and/or clients
Managed LLM platforms (Bedrock, Anthropic, OpenAI, Azure OpenAI)
Production vector databases (Qdrant, pgvector, Weaviate, Pinecone)
Modern frontend work (React or similar) for building internal tools and agent UIs
SSO / OIDC and role-based access control patterns
Prompt engineering and structured-output techniques (tool use, JSON schema, function calling)
Experience operating GPU workloads on Linux
Prior technical leadership or tech-lead experience on AI / data products
Self-hosted Linux platform with GPU acceleration for local model inference
Containerized services with shared internal networking
AI coding agents available and encouraged for day-to-day development
Local-first development with simple bypasses for auth and external dependencies
An applied AI engineer who ships — comfortable owning a feature from prompt design through production rollout
Someone who treats LLMs as one component in a system, not the whole system — strong fundamentals in APIs, data, and infrastructure
An engineer who is genuinely fluent with agentic coding tools and can demonstrate how they use them to multiply their output
A collaborator who can translate fuzzy stakeholder requests into concrete agent and application designs
Final level and compensation will be determined based on the candidate’s skills, experience, qualifications, and internal equity.
Discretionary bonus program
Future financial security with a 401(k) program with company match
Paid time off covering vacations, personal time off and sick days, capped off by an exciting year-end holiday shutdown
We are seeking an experienced AI Developer to design, build, and scale production AI applications and autonomous agents on a modern, self-hosted platform. This role will play a key part in shaping the architecture, tooling, and engineering standards that power AI capabilities across the organization. Working across the full technology stack, the AI Developer will leverage large language models and AI coding agents as core components of the development process to deliver reliable, high-impact solutions. This is a highly influential, hands-on engineering role focused on building practical, production-ready AI systems in a fast-moving and collaborative environment.
This individual is equally comfortable discussing architecture and implementing solutions, with a strong sense of ownership and a desire to influence platform direction, tooling decisions, and engineering best practices. They are curious, adaptable, and energized by solving complex problems while building scalable, maintainable systems. Strong collaboration and communication skills are essential, as this role partners closely with technical and cross-functional teams to bring AI capabilities into production.
Design and implement AI-powered services, agents, and internal applications
Develop end-to-end features — backend services, data pipelines, and user-facing UIs — using agent-assisted workflows
Design retrieval pipelines (chunking, embeddings, vector search) and prompt strategies
Enforce shared platform conventions: authentication, RBAC, container networking, secrets handling
Mentor other engineers on applied AI patterns and on getting the most out of agentic coding tools
10+ years of experience in software engineering
2+ years working directly with AI/ML or LLM-based applications
Strong software engineering fundamentals) — systems thinking, debugging, API design, data modeling
Production experience shipping web services and/or data products in any modern stack
Demonstrated fluency with AI coding agents (Claude Code, Cursor, Copilot, Aider, or similar) as part of daily work
Solid SQL and relational data modeling skills
A pragmatic approach to tests, observability, and operational quality
Model Context Protocol (MCP) — building servers and/or clients
Managed LLM platforms (Bedrock, Anthropic, OpenAI, Azure OpenAI)
Production vector databases (Qdrant, pgvector, Weaviate, Pinecone)
Modern frontend work (React or similar) for building internal tools and agent UIs
SSO / OIDC and role-based access control patterns
Prompt engineering and structured-output techniques (tool use, JSON schema, function calling)
Experience operating GPU workloads on Linux
Prior technical leadership or tech-lead experience on AI / data products
Self-hosted Linux platform with GPU acceleration for local model inference
Containerized services with shared internal networking
AI coding agents available and encouraged for day-to-day development
Local-first development with simple bypasses for auth and external dependencies
An applied AI engineer who ships — comfortable owning a feature from prompt design through production rollout
Someone who treats LLMs as one component in a system, not the whole system — strong fundamentals in APIs, data, and infrastructure
An engineer who is genuinely fluent with agentic coding tools and can demonstrate how they use them to multiply their output
A collaborator who can translate fuzzy stakeholder requests into concrete agent and application designs
Final level and compensation will be determined based on the candidate’s skills, experience, qualifications, and internal equity.
Discretionary bonus program
Future financial security with a 401(k) program with company match
Paid time off covering vacations, personal time off and sick days, capped off by an exciting year-end holiday shutdown