GenAI Architect
- Status
- Open
- Remote policy
- Remote
- Employment type
- Not stated
- Salary
- Not stated
- Categories
- Gen-AI-Architect, AI-ML-Architecture, Senior-AI-Engineer, AI-Platform-Engineering, LLM-Engineering, Generative-AI-Architect, GenAI-Solutions-Architect, Generative-AI-Architecture, AI-Agent-Architect, AI-Architecture, Agentic-AI-Architect
- Source
- himalayas
- First observed
- 2026-09-03 21:00 UTC
- Last seen
- 2026-09-03 21:00 UTC
- Source claims posted
- 2026-09-03 20:54 UTC
- Consecutive misses
- 0 of 10
What the posting says
This is a remote position.
We are looking for a Senior AI Engineer to join a team building and operating a production-grade LLM system used by real users. This is not a proof-of-concept project.
You will be responsible for taking LLM-powered features from idea through development and deployment to continuous improvement, with a strong focus on answer quality, performance, observability and cost efficiency. You will work with modern LLM technologies, including LangGraph, RAG, tool calling, Azure OpenAI, Gemini and Claude, and have a real impact on how AI-powered products are built and operated in production.
Responsibilities:
Build LLM-powered features end to end. Design and implement agentic flows, retrieval, and tool calling using LangGraph - then ship them as FastAPI services with streaming, persistence and proper tests.
Own answer quality. Build evaluation datasets, regression suites and LLM-as-judge checks so we know whether a prompt or model change made things better before it reaches users.
Get the right context to the model. Turn user questions into effective queries against our search platform, orchestrate multi-step research loops, and shape the context the model reasons over. When an answer is wrong, work out whether retrieval, the query or the prompt is at fault - and fix the right one.
Debug production. Instrument flows with tracing (Langfuse), investigate bad answers from real traces, and manage latency, token and cost budgets - including routing across model sizes and families behind an AI gateway.
Requirements
Experience in building and operating backend services - APIs, async, testing
Hands-on experience taking LLM features to production and keeping them running - not only prototypes
Agent / orchestration frameworks - LangGraph ideally
Practical RAG experience
Experience debugging LLM systems in production - tracing, evaluation, cost and latency
Experience running services in the cloud (we're on Azure)
Strong problem-solving skills, analytical thinking, and technical decision-making
Fluent in English, proactive communicator, and a collaborative team player
Open-minded, creative, and motivated to push boundaries in AI and automation
Nice to have:
Azure OpenAI, AI Search, App Service
Infrastructure-as-code (Bicep)
Mentoring or tech-lead experience
Tech stack:
Python
FastAPI
LangGraph
Azure OpenAI, Google Gemini, Anthropic Claude
FAISS
PostgreSQL
Langfuse
Azure App Insights
Bicep
Docker/Podman
Originally posted on Himalayas
Quality
- x Salary range stated weight 35%
- + Remote policy stated weight 20%
- + Location stated weight 15%
- + Organisation stated weight 15%
- + Publication date stated weight 15%
Not enough history yet to judge honesty signals.
Timeline
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#555540 2026-09-03 21:00 UTCPublished