4+ years of software engineering experience, including 1-2 years with production LLM systems.
Hands-on experience with knowledge ingestion and retrieval systems across multiple modalities.
Strong Python skills and backend engineering fundamentals, particularly in cloud environments (AWS preferred).
Metrics-driven approach to building evaluations and benchmarks for LLM systems.
Proven ability to transition ambiguous problems into reliable production services.
Experience with CI/CD, release management, and monitoring of production systems.
Responsibilities
Design and build multimodal knowledge base ingestion pipelines for diverse data types.
Enhance retrieval-augmented generation (RAG) systems through advanced indexing and context assembly.
Manage the knowledge base lifecycle, ensuring continuous improvement and validation.
Capture user feedback to drive measurable improvements in AI performance.
Oversee deployment and delivery of AI system updates across various environments.
Optimize reliability and latency of AI features through profiling and caching strategies.
Collaborate with engineers to integrate knowledge bases with 3D scene representations.
Benefits
Opportunity to work on cutting-edge AI systems with real-world applications.
Collaborative, cross-functional team environment.
Focus on continuous improvement and innovation in AI technology.
Exposure to diverse deployment environments, enhancing technical versatility.
Potential for involvement in high-impact projects within regulated industries.
Full Job Description
About the Role
We are seeking a highly skilled LLM Platform Engineer to join our team full-time. You will play a foundational role in designing, building and optimizing the AI systems that turn heterogeneous knowledge - technical documentation, video, imagery, voice, and 3D scene data - into grounded, real-time guidance for next-generation training and maintenance applications.
This is a high-impact, cross-functional role: you will work end-to-end from evaluating emerging methods to production inference and performance optimization, ensuring our platform retrieves the right knowledge, reasons over it reliably, and responds in real time across diverse deployment environments.
Core Responsibilities
Design and build multimodal knowledge base ingestion: pipelines that convert documents, video, images, audio, and structured data into a unified, queryable semantic layer with rich embeddings and metadata, including bringing in external sources and messy real-world formats as they arise.
Improve our retrieval-augmented generation (RAG) systems: chunking and indexing strategies, hybrid retrieval, reranking, grounding controls, and context assembly for multimodal queries.
Build the knowledge base lifecycle: handle versioned documentation updates, incorporate SME corrections and annotations, and design validation and freshness mechanisms so the knowledge base improves continuously.
Scale the feedback loop: capture user signals (ratings, corrections, query patterns), route them into measurable improvements to retrieval and content, and build a durable picture of how the AI is performing in the field.
Own the deployment and delivery lifecycle of AI system updates across cloud, on-premises, and air-gapped environments: packaging, release management, configuration, and monitoring.
Strengthen the reliability and latency of AI features: profiling inference paths, caching, fallback behavior, and graceful degradation in production.
Collaborate with spatial computing, mobile, and product engineers to connect the knowledge base with 3D scene representations and ship mission-critical features.
Essential Skills & Experience
4+ years of software engineering experience, with at least 1-2 years building production LLM systems (RAG pipelines, agents, or LLM-powered products with real users).
Hands-on experience designing knowledge ingestion and retrieval systems: embedding models, vector and hybrid search, chunking strategies, and semantic data modeling across more than one modality.
Strong Python and backend engineering fundamentals: APIs, data pipelines, async systems, and working in a cloud environment (AWS preferred).
A metrics-driven approach: experience building evals or benchmarks for LLM systems and using them to drive iteration, not just report scores.
Demonstrated ability to take ambiguous problems from prototype to reliable, maintainable production services.
Experience deploying and operating production systems: CI/CD, release management, and monitoring.
Nice to Have
Experience fine-tuning open-weight models (LoRA/PEFT, distillation) or deploying quantized models for offline, edge, or on-premises environments.
Research background or publications in retrieval, multimodal learning, or agentic systems, or a track record of translating recent research into shipped capabilities.
