AI Engineering
AI Engineering job. Build your future now, not later
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AI Engineering job
- 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.
- 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).
- 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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Eduardo B.
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Data Analyst
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