Senior Python AI Engineer

• 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.

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