C

AI Engineer - Production LLM Systems

conquer ai • United State
Remote
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AI Summary

Deploy production-ready AI systems for FTSE 100, FTSE 250 and Fortune 500 clients. Build and maintain RAG pipelines, integrate and fine-tune LLMs, and create APIs for engineering teams. Requires 3+ years of software/ML engineering with recent AI/LLM project experience.

Key Highlights
Take LLM prototypes from concept to production deployment
Build RAG pipelines with vector databases
Integrate and fine-tune OpenAI, Gemini or open-source LLMs
Develop APIs for engineering team consumption
Key Responsibilities
Build RAG pipelines on vector databases
Integrate and fine-tune models (OpenAI, Gemini or open-source LLMs)
Wrap AI capabilities in APIs for wider engineering team
Ensure prompt quality and safety including model output evaluation
Technical Skills Required
Python RAG architecture Vector databases API development
Benefits & Perks
Direct exposure to live production AI/LLM deployment
Competitive contract package
Nice to Have
MLOps experience (evaluation pipelines, monitoring, versioning)
PyTorch experience with fine-tuning or training

Job Description


AI Engineers


Fully Remote

Contract 6 months

Day Rate


Conquer AI deploys production-ready AI systems for FTSE 100, FTSE 250 and Fortune 500 clients. We're scaling fast. A client in America needs AI Engineers who can take an LLM idea from prototype to a system running in production, sitting inside their engineering team.


The reality of this role Most AI prototypes never make it past the demo. This client needs someone who can take one the rest of the way, into production, and keep it running there. You'll build RAG pipelines, integrate and fine-tune models, and wrap the whole thing in APIs the wider engineering team can use. If the output drifts or the safety checks are thin, it doesn't ship. You'll be on-site with the client's own engineers, not presenting from a deck.


What you'll own

-LLM-powered applications and agentic workflows, from design through to deployment

-RAG pipelines built on vector databases

-Model integration and fine-tuning (OpenAI, Gemini or open-source LLMs)

-APIs that put AI capabilities in reach of the wider engineering team

-Prompt quality and safety, including how we evaluate model output


Who you are

-3+ years in software or ML engineering, with recent AI/LLM project work

-Strong Python, plus LangChain, LlamaIndex or similar

-Comfortable with vector databases (Pinecone, Weaviate or similar)

-You've integrated third-party model APIs (OpenAI, Gemini, Anthropic) before

-You understand RAG architecture and fine-tuning, not just prompt engineering

-PyTorch experience with fine-tuning or training is a plusMLOps experience (evaluation pipelines, monitoring, versioning) is a plus


Where you're coming from AI Engineer, ML Engineer or Software Engineer who's moved into applied AI and LLM work. You've shipped something real, not just a demo. Now you want to do it on-site, inside a client's own team.


What we're offering

-Direct exposure to a live production AI/LLM deployment

-Competitive contract package


If you can take an LLM idea from prototype to production without hand-holding, we should talk.


PLEASE NOTE I personally review every CV, message and email I receive regarding our vacancies. While I read each one carefully, I'm unable to reply to everyone individually due to the volume of applications. Thank you for taking the time to apply and for your interest in working with us. Your effort is genuinely appreciated.


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