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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior AI Engineer (Xora Portfolio Company) - **Company:** XORA INNOVATION USA INC. - **Location:** San Diego, CA, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Training Data, Application Programming Interfaces (APIs), Artificial Intelligence, Code Review, Graph Database, Interoperability, JSON, Python (Programming Language), Open Source Technology, Technical Data Management Systems, Large Language Models, Information Technology, Low Latency - **Published:** August 4, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=cc0a4b6718975895 ## About the Role * Bachelor's or Master's degree in Computer Science or a related engineering field, and 5+ years building and shipping production software, including deep hands-on work building LLM-powered systems in production. * Strong Python and a track record of shipping reliable services: async, HTTP and streaming APIs, testing, code review. * Production experience with LLMs: prompting and context engineering, tool calling, structured output, and the latency and cost work that keeps them usable. * Hands-on experience designing and shipping agents: the loop, the tools, context, memory, and where they fail. A framework such as LangGraph or equivalent; structured outputs in Pydantic or JSON Schema. * Experience building RAG systems: embeddings, chunking, hybrid search, reranking, and a feel for what actually moves retrieval quality. * Direct experience fine-tuning open-weight models (LoRA, QLoRA, or full-parameter) on multi-GPU, including curating and formatting the training data. * Experience with LLM evaluation and guardrails: LLM-as-judge or automated scoring, regression tracking, and tracing over agent runs. * Experience building shared LLM tooling or platform components that other engineers build on, and comfort owning ambiguous systems end to end in an early-stage environment. NICE TO HAVE * Self-hosted inference with vLLM, TGI, or SGLang, served behind an OpenAI-compatible interface. * Interoperability standards for tools and agents, such as MCP. * Retrieval over structured data: knowledge graphs, hybrid search, reranking at scale. * LLMs applied to scientific or other technical data; experience making APIs and tool surfaces easy for agents to call reliably. * Contributions to open-source AI/ML: agent frameworks, eval tooling, RAG, fine-tuned models. ## Description This role owns the LLM systems behind our platform: the agents and fine-tuned models that ship as product, and the engineering that keeps them reliable - evaluation, tracing, and production-quality services. It's deeply hands-on, from model internals to shipped software. The platform runs inside our customers' own secure environments: their compute, their cloud, or a hybrid. So the LLM layer has to work with commercial APIs and self-hosted models alike, and carry its own safeguards wherever it lands. Every LLM capability we ship stands on this work. WHAT YOU WILL DO * Build and ship LLM-powered capabilities end to end: prototype, evaluate, deploy, and iterate them into production services users rely on. * Design agents that plan and carry out multi-step work: tool calling, structured outputs, durable state, and the judgment to know when an agent is the wrong tool. * Build retrieval that gives models the right context: ingestion, chunking, embeddings, hybrid search, reranking. * Fine-tune open-weight models with LoRA, QLoRA, or full-parameter tuning on multi-GPU, curating the training data and choosing the method by task, compute budget, and target. * Build evaluation loops that gate what ships: automated scoring, LLM-as-judge, and regression tracking against curated test sets. * Instrument model calls and tool use with tracing, so quality, cost, and failures stay debuggable in production. * Turn LLM capabilities into clean APIs and reusable tooling that other engineers build on. ## Related Videos - [Tips and Tricks for Working with JSON](https://www.wearedevelopers.com/videos/1229-tips-and-tricks-for-working-with-json) - [Are Code Reviews Worth It? Insights from 16 Years of Review Data](https://www.wearedevelopers.com/videos/1135-are-code-reviews-worth-it-insights-from-16-years-of-review-data) - [Swapping Low Latency Data Storage Under High Load](https://www.wearedevelopers.com/videos/746-swapping-low-latency-data-storage-under-high-load) - [AX is the only Experience that Matters](https://www.wearedevelopers.com/videos/1404-ax-is-the-only-experience-that-matters) - [Introducing JSON Structure](https://www.wearedevelopers.com/videos/100219-introducing-json-structure) - [One AI API to Power Them All](https://www.wearedevelopers.com/videos/1601-one-ai-api-to-power-them-all) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Everything a Developer Needs to Know About MCP with Neo4j](https://www.wearedevelopers.com/magazine/604-everything-a-developer-needs-to-know-about-mcp-with-neo4j) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)