Role: AI Engineer/Senior AI Engineer (Agentic Systems & LLM Integration)Location: Santiago, ChileEmployment type: Full Time (Hybrid)Role SummaryKR Elixir Technology are hiring an experienced engineer to design, build, and operate production-gradeagentic AI systems and LLM integrations. The role owns the technical implementation of multi-agentarchitectures, retrieval-augmented generation (RAG) pipelines, tool/function calling, evaluationframeworks, and session/state management so that LLM-based applications behave reliably and safely incustomer environments.Key Responsibilities• Design and implement multi-agent architectures and runtime components that persistconversation/session state (checkpointers/persistence layers).• Build and operate RAG pipelines, including vector search, reranking (cross-encoder orequivalent), and context hygiene to reduce hallucinations.• Implement function/tool-calling integrations (API/function binding, JSON schema outputs,runtimes that execute and feed results back to LLMs).• Create evaluation and testing frameworks (e.g., LLM-as-a-Judge workflows, golden datasets,rubrics) for automated grading of model outputs.• Design and manage conversation history / context window strategies and session retrieval somulti-turn references resolve correctly.• Work with engineers and infra teams to integrate LLMs, vector DBs, databases (Postgres), andmonitoring/telemetry for production resilience.• Produce clear tech designs, runbooks, and unit/integration tests for agent components; enablereproducible failures and component-level observability.• Collaborate closely with product, data science, and QA on deployment, A/B testing, andperformance tuning.Required Skills & Experience• Proven experience building production LLM/agent applications or RAG systems.• Hands-on with agent frameworks and persistence concepts (e.g.,Checkpointer/MemorySaver/PostgresSaver patterns or equivalent).• Practical experience with vector search and reranking techniques (cross-encoders, two-stageretrieval).• Experience implementing function/tool calling patterns and runtime integrations.• Familiarity with evaluation approaches for LLMs (LLM-as-a-Judge or programmatic evaluationpipelines).• Strong knowledge of session state, context windows, and chat history management.• Comfortable with backend systems: REST/GraphQL APIs, Postgres (or equivalent), vector DBs,and cloud deployments.• Solid software engineering fundamentals: testing, CI/CD, observability, and fault isolation.• Excellent debugging and systems-thinking skills to locate failures at component level.Preferred / Nice-to-have• Experience with specific agent/LLM frameworks (LangGraph, LangChain, AutoGen, etc.).• Familiarity with cross-encoder models, embedding pipelines and popular vector stores.• Prior work on production monitoring for LLM-based apps (latency, cost controls, hallucinationmetrics).• Background in designing human-in-the-loop fallbacks and safety/guardrails for autonomousagents.