360Learning
Tech / AI / Software
AIEngineer
Neural analysis suggests this role is
optimal for Mid candidates.
“AI Engineer at 360Learning. Skills: GenAI, LLM, Python, Azure OpenAI, OpenAI, Anthropic, Mistral, RAG, semantic search, vector databases, Langfuse, Machine Learning. Lead real and complex technical challenges. Analysis and modeling capabilities”
Industry & Context.
Analysis and modeling capabilities are key; Optimize for real-world constraints: reduce per-request costs, improve latency, scale to handle usage spikes; Make architectural decisions as we scale from 5 to 10+ features
What They're Looking For.
Must Have
2 years of experience taking GenAI / LLM-powered systems from prototype to long-term production deployment, Python experience, with a track record of clean, maintainable production code, Hands-on experience integrating LLM (Azure OpenAI, OpenAI, Anthropic, Mistral, etc. ) and managing real-world constraints: token costs, latency, reliability, hallucinations, Familiarity with Search capabilities, RAG, semantic search, vector databases, Agents, LLM observability tools (Langfuse or similar), Evaluation of models, Solid grasp of Machine Learning fundamentals (evaluation, experimentation, model lifecycle), Fluent English (US/UK) / B2 level or equivalent (FR)
Nice to Have
Knowledge of Pydantic AI, FastAPI, Azure OpenAI, ElasticSearch, New modalities, real-time collaboration
What You'll Do.
Lead real and complex technical challenges
Analysis and modeling capabilities
Manage considerable volume of data
Contribute meaningfully to an existing feature
Own end-to-end delivery of a new AI feature: from technical design through production launch
Optimize for real-world constraints: reduce per-request costs
scale to handle usage spikes
Make architectural decisions as we scale from 5 to 10+ features
Prototype and ship advanced capabilities: new modalities
real-time collaboration
How You'll Work.
Team & Collaboration
Decentralized peer review process; Qualitative and regular feedback from other team members; Promote pair programming and knowledge sharing; Discovery Meeting with an AI Engineer; Case Study and Clarification Meeting with an AI Engineer and the Product Manager
Communication Scope
Fluent English (US/UK) / B2 level or equivalent (FR)
Process & Methodology
Own end-to-end delivery of a new AI feature: from technical design through production launch, Make architectural decisions
Full Job Description
## Description At the start of 360Learning's growth, we were only 10 developers in the R&D department working on a single codebase. Today, we are a team of 60+ engineers divided into 10 product squads. Each squad includes developers, product managers and designers. Our technical teams are paramount to 360Learning’s response to our increasingly demanding customers and are strategic players for the growth of the company. We have the opportunity to: Lead real and complex technical challenges: A complex codebase on which analysis and modeling capabilities are key. Significant traffic (2.3M registered users, 200K unique monthly visitors) with a considerable volume of data to manage. A strong focus on “clean architecture” for long-term growth. Work on an attractive technical stack: We work on MongoDB, Node.js and Vue.js, three of the most popular JS technologies on the market. We are currently migrating to TypeScript. Grow within a R&D team that allows rapid progress: Our decentralized peer review process provides us with qualitative and regular feedback from other team members. We promote pair programming and knowledge sharing. ## Within 1 month, you will Graduate in Convexity through our onboarding process Understand our product offering through training Meet the team through virtual coffee meets and happy hour Learn our processes and tools ## Within 3 months, you will Get a global view of our codebase and architecture (Pydantic AI, FastAPI, Azure OpenAI, ElasticSearch) Contribute meaningfully to an existing feature ## Within 6 months, you will Own end-to-end delivery of a new AI feature: from technical design through production launch Optimize for real-world constraints: reduce per-request costs, improve latency, scale to handle usage spikes ## Within 12 months, you will Make architectural decisions as we scale from 5 to 10+ features Prototype and ship advanced capabilities: new modalities, real-time collaboration Be the technical authority from your peer ## The
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