Zup Innovation
Senior Artificial Intelligence Engineer
About this role
Our mission is to create technologies that rival the world’s best and are game-changers for our clients. That’s why we’re looking for professionals who want to be part of a culture driven by excellence and innovation. If you thrive in a collaborative, curiosity-driven work environment, come build the future of technology with us. We value the continuous growth of our team members, encouraging each person to pursue paths that drive their professional development. If you have in-depth experience in AI, know how to deploy models and agents in cloud environments, and want to play a leading role in sharing knowledge with the entire development community, this opportunity is for you. We’re looking for someone passionate about Artificial Intelligence, with a technical background and advocacy skills, to join the team responsible for creating, evolving, and disseminating AI security guardrails! Work with cutting-edge technologies, help protect intelligent systems, and actively contribute to our company’s security culture. What you’ll do here
- Design, develop, and implement AI agents focused on security solutions, using market-leading frameworks and tools;
- Integrate and operationalize large language models (LLMs) and knowledge bases, automating and optimizing critical processes using Python and modern APIs;
- Evaluate and suggest alternatives on GenAI platforms (such as AWS Bedrock, Azure AI, Google Vertex AI) for the customized creation of intelligent agents, taking scalability and security into account;
- Implement and query vector databases (e.g., Pinecone, Weaviate, Milvus, ChromaDB) to ensure efficient storage and retrieval of information;
- Serve as an AI advocate, promoting training sessions and talks, and sharing best practices for AI security with other development teams;
- Automate deployment and monitoring processes for agents and models in cloud environments, using tools such as Docker, Kubernetes, Terraform, and CI/CD. What We Expect You to Know
- Advanced Python programming.
- Hands-on experience with LLMs: API integration, model evaluation, and context management.
- Implementation of RAG pipelines using vector databases (e.g., Pinecone, Weaviate, pgvector) and frameworks such as LangChain or LlamaIndex.
- Proficiency in prompt engineering techniques: few-shot, chain-of-thought, structured output, and instruction strategies. Prompt Engineering
- Definition and application of sophisticated algorithms tailored to AI contexts.