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AI Engineer / Architect (Agentic RAG)
Aguascalientes, Ags., MXPosted 1 months ago
hybrid
Job Description
AI Engineer / Architect (Agentic RAG) Senior
Requirements
- Bachelor's or Master's degree in Computer Science, Data Science, AI/ML, or related field.
- 5+ years of experience in AI engineering or architecture, with proven expertise in RAG implementations.
- Strong proficiency in Python, C#, or Java, with hands-on experience in ML frameworks (TensorFlow, PyTorch, Hugging Face).
- Experience with vector databases (e.g., Pinecone, Weaviate, Milvus) and embedding models.
- Solid understanding of LLM orchestration frameworks (LangChain, Semantic Kernel, or equivalent).
- Knowledge of cloud platforms (Azure, AWS, GCP) and containerization (Docker, Kubernetes).
- Strong problem-solving skills and ability to design scalable, production-ready AI systems.
- Excellent communication skills for presenting architectural solutions to both technical and executive stakeholders.
- Preferred Skills
- Experience with agent orchestration tools and multi-agent systems.
- Familiarity with knowledge graphs, semantic search, and hybrid retrieval methods.
- Background in DataOps or MLOps, ensuring smooth deployment and monitoring of AI systems.
- Bilingual communication (Spanish/English) is a plus for global collaboration.
- AI Engineer / Architect with deep expertise in designing and implementing advanced AI systems, particularly those leveraging Agentic RAG (Retrieval-Augmented Generation). The ideal candidate will combine strong technical skills in AI/ML engineering with architectural vision, enabling scalable, intelligent, and context-aware solutions. This role requires hands-on development, system design, and collaboration across multidisciplinary teams to deliver cutting-edge AI capabilities.
Responsabilities
- Architect and implement Agentic RAG solutions, integrating retrieval mechanisms with generative models to enhance accuracy, adaptability, and contextual relevance.
- Design and optimize AI pipelines for data ingestion, indexing, retrieval, and generation, ensuring scalability and performance.
- Develop reusable AI components and frameworks that support agent orchestration, multi-step reasoning, and dynamic workflows.
- Collaborate with data engineers, ML scientists, and product teams to align AI solutions with business objectives and operational requirements.
- Evaluate and integrate vector databases, embeddings, and retrieval strategies to maximize system efficiency.
- Conduct experiments, benchmarking, and performance tuning of RAG-based architectures.
- Define and enforce best practices for AI system design, including security, compliance, and ethical considerations.
- Stay current with emerging trends in agentic AI, LLM orchestration, and retrieval-augmented architectures.
Languages
Fluent Advanced (96-100%)
Remote