Senior AI Engineer (LLM)

ProxifyProxify·Remote(Time zone: CET (+/- 3 hours))
AI & Machine Learning

WFA Digital Insight

As demand for AI-powered solutions grows, Proxify seeks a Senior AI Engineer to lead the charge. With a strong background in Python and Large Language Models, candidates can capitalize on the 25% increase in AI job postings in 2025. Proxify's commitment to remote work and developer success sets it apart, but candidates should be prepared to showcase expertise in prompt engineering and RAG architecture.

Job Description

About the Role

Proxify is seeking a Senior AI Engineer to lead the development and scaling of LLM-powered solutions. The ideal candidate has a foundation in Python and expertise in the generative AI lifecycle, from prompt engineering to production-grade deployment.

Responsibilities

  • Architect and implement complex AI workflows using frameworks like LangChain, LlamaIndex, or Haystack
  • Design and optimize Retrieval-Augmented Generation (RAG) pipelines, including vector database management and advanced indexing strategies
  • Evaluate and implement strategies for model selection based on latency, cost, and performance requirements
  • Develop high-performance Python backends using FastAPI or Flask

Requirements

  • Expert-level Python skills with a deep understanding of asynchronous programming and backend architecture
  • Proven experience building and deploying production-level applications using Large Language Models (LLMs)
  • Strong experience with SQL and NoSQL databases, specifically optimized for high-dimensional vector search

How to Stand Out

  • Showcase your expertise in Python and AI by highlighting projects that demonstrate prompt engineering and RAG architecture skills
  • Prepare to discuss your experience with Large Language Models and how you've applied them in previous roles
  • Develop a strong understanding of cloud environments like AWS, GCP, or Azure, and be ready to discuss deployment strategies
  • Create a portfolio that highlights your ability to architect and implement complex AI workflows
  • Be prepared to discuss your approach to model selection and how you evaluate latency, cost, and performance requirements

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