Open role · Engineering

Software Engineer, AI Product

Work mode: Remote (EST preferred) Engagement type: Full-time Contract Req ID: GDBI-4N6P8K2M

Role summary

Build production AI capabilities for NoèmeX products using LLMs, embeddings, semantic search and intelligent workflow patterns grounded in enterprise data.

What you’ll do

Prototype and productionize AI-assisted analytics, semantic search and natural-language data experiences.
Integrate LLMs, embeddings, vector databases and ML models into end-user product workflows.
Build evaluation, observability and cost-control mechanisms for AI features.
Create secure RAG, prompt-management and orchestration patterns for enterprise use cases.
Work across frontend, backend and data layers to deliver complete AI-enabled product experiences.
Partner with Product, Design, Data Engineering and ML teams on responsible AI delivery.

What you should bring

Software engineering experience with meaningful production work in AI/ML products.
Hands-on experience with LLM APIs, embeddings, vector search and modern AI application patterns.
Strong full-stack skills and the ability to move between UI, backend services and data pipelines.
Understanding of model limitations, evaluation, latency, privacy and cost trade-offs.
Strong Python and/or Node.js skills plus modern frontend experience.
Ability to translate uncertain product ideas into testable, production-ready systems.

Useful additional experience

RAG and agent frameworks
Vector databases and search infrastructure
MLOps / LLMOps experience
Enterprise security or governance experience

How we work

NoèmeX teams stay close to the business problem and the production system. You may work across client delivery, platform development or internal capabilities depending on the role. We value clear specifications, maintainable architecture, disciplined execution and direct communication.

Hiring process

The exact assessment varies by role. Shortlisted candidates should expect an initial conversation followed by a role-relevant technical, practical or structured panel assessment before a final decision.

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