Key skills
AWSAzureCloudDockerGoogle Cloud PlatformOpen SourcePythonReactSQLAIMachine LearningNLPGenerative AILLMRAGLangChainLlamaIndexMLOpsChromaPineconeWeaviateData EngineeringGCPGoogle CloudGitCI/CD
About this role
Role Overview
- Design and implement LLM-based agent architectures;
- Develop and evolve RAG pipelines (ingestion, embeddings, chunking, retrieval and ranking);
- Build agents capable of tool orchestration (tool calling);
- Integrate AI solutions with APIs and enterprise systems;
- Implement prompt engineering strategies and response control;
- Define evaluation metrics and monitor agent performance;
- Work on mitigating hallucinations and implementing guardrails;
- Support technical decisions related to architecture and scalability;
- Collaborate with multidisciplinary teams (engineering, product, business);
- Contribute to MLOps best practices and AI governance.
Requirements
- Minimum of 5 years of experience in Data Science or Machine Learning;
- Experience in end-to-end data projects, from exploration and modeling to deployment and monitoring in production environments;
- Strong experience with Python;
- Advanced knowledge of Machine Learning and NLP;
- Hands-on experience with NLP and applied AI, including development, implementation, and maintenance of production solutions;
- Practical experience with LLMs and Generative AI;
- Development or participation in building agents with tool orchestration (e.g., ReAct approach);
- Implementation of RAG architectures;
- Experience implementing and using embeddings and vector databases (FAISS, Pinecone, Weaviate, Chroma or similar);
- Prompt engineering techniques and response evaluation;
- Mitigation of hallucinations and implementation of guardrails;
- Experience with agent frameworks (LangChain, LlamaIndex or similar);
- Knowledge of ReAct architecture (Reason + Act);
- Development and consumption of REST APIs;
- Experience with Docker, Git, and CI/CD;
- Experience in Cloud environments (AWS, Azure or GCP);
- Advanced SQL;
- Knowledge of model evaluation and performance metrics;
- Experience with model deployment and monitoring;
- Technical English for reading;
- Degree in Computer Science, Computer Engineering, Data Engineering, or related fields.
- Previous experience with enterprise agents;
- Knowledge of MLOps;
- Experience with multi-agent systems;
- Experience with real-time data pipelines;
- Experience in regulated environments;
- Participation in open source projects or publications in the field.
Tech Stack
- AWS
- Azure
- Cloud
- Docker
- Google Cloud Platform
- Open Source
- Python
- React
- SQL
Benefits
- 100% remote.