Proxify AB: Senior Python AI Engineer

The Role:

We are looking for a Senior Python AI Engineer to join our fast-growing Network, who will design and develop backend systems and APIs for AI-powered applications. You will play a key role in designing and building scalable backend systems and APIs, collaborating closely with cross-functional teams to shape the future of data-driven products across various platforms.

What we are looking for:

• Strong proficiency in Python (5+ years), including modern frameworks (FastAPI, Flask, or Django).
• Deep learning frameworks (PyTorch, TensorFlow) for custom modeling beyond LLM APIs.
• Experience with large language models (LLMs) such as GPT, Gemini, LLaMA, or similar.
• Experience with prototyping tools: Streamlit, Gradio
• Solid experience designing RESTful APIs and microservice architectures.
• Strong backend development expertise, including databases (SQL/NoSQL).
• Experience with version control (Git) and CI/CD workflows.
• Hands-on experience with containerization (Docker, ideally Kubernetes).
• Familiarity with cloud platforms (AWS, Azure, or GCP) is a plus.
• Understanding of security best practices for handling sensitive data.
• Strong problem-solving skills to address complex challenges and performance bottlenecks.
• Excellent technical communication skills to collaborate effectively across teams and explain technical concepts to non-technical stakeholders.
• Ability to work independently while aligning with broader team goals.
• Intermediate-advanced English level.
• Time zone: CET (+/- 3 hours). We are unable to consider applications from candidates in other time zones.

AI/ML & LLM Ecosystem:

• LLM orchestration frameworks: LangChain, LangGraph, LlamaIndex.
• Retrieval-Augmented Generation (RAG) pipeline design.
• Experience with vector databases (Pinecone, Weaviate, Milvus, Chroma, FAISS).
• Hands-on with LLMs & APIs: OpenAI (GPT-5/5-mini), Anthropic Claude, Google Gemini, Meta Llama, Mistral.
• Familiarity with AWS Bedrock for accessing and deploying foundation models.
• Prompt engineering and structured output design (JSON mode, function calling).
• Model fine-tuning (LoRA, QLoRA) and evaluation frameworks (DeepEval, Ragas).

Responsibilities:

• Design and develop backend systems and APIs for AI-powered applications.
• Build and optimize LLM-based workflows, including chatbots, copilots, and automation tools.
• Implement RAG architectures using vector databases and document pipelines.
• Integrate and orchestrate cloud-hosted foundation models (AWS Bedrock, OpenAI, Anthropic, Google Gemini, Meta Llama, Mistral).
• Collaborate cross-functionally with data scientists, product managers, and frontend developers to deliver end-to-end AI products.
• Ensure performance, scalability, and cost optimization of AI solutions in production environments.
• Monitor, evaluate, and continuously improve deployed AI systems.