AI Engineer / Architect (Agentic RAG)
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AI Summary
Architect and implement Agentic RAG solutions and optimize AI pipelines for data ingestion and retrieval. Develop reusable AI components for agent orchestration and collaborate with multidisciplinary teams to align AI solutions with business goals.
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
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