Job Description

​ We are seeking an Expert AI Engineer to own the design, architecture, and deployment of agentic AI systems for a bilingual enterprise knowledge platform built on large language models. You will define the technical direction for autonomous, multi-agent, and multi-step workflows—incorporating planning, reasoning, memory, and tool use—on top of production-grade Retrieval-Augmented Generation (RAG) pipelines, along with the workflow, integration, and background processing layers that support them. This role carries deep end-to-end ownership of architecture, quality, and performance. Responsibilities Architect agentic systems incorporating query planning, reasoning, memory, tool use, and multi-step task execution Define orchestration patterns that coordinate agents, retrieval, tools, and LLM calls into reliable, observable autonomous pipelines Design robust single- and multi-agent architectures with state management, control flow, error recovery, and guardrails Architect the workflow layer: composable, versioned workflows combining deterministic steps, agentic branches, conditional routing, and human-in-the-loop checkpoints, with durable state, checkpointing, and retries Design the connector and integration layer for enterprise content sources and APIs, covering authentication, incremental sync, content normalization, and permission-aware retrieval Own the background processing layer, including scheduled ingestion, index and embedding refresh, job queuing and concurrency control, failure recovery, and content-freshness monitoring Own and optimize the underlying RAG layer spanning chunking, embedding, retrieval, reranking, and grounded generation Implement and tune dense, sparse, hybrid, and metadata-based retrieval using vector databases and BM25 Establish prompting, grounding, and verification strategies to ensure responses remain accurate and citation-backed Define evaluation frameworks and quality gates; drive continuous improvement across task success, relevance, latency, and reliability Requirements Deep, demonstrated expertise in agentic AI and LLM-based applications, with a track record of production systems Expert Python proficiency and a strong software engineering foundation, including production deployment and system design Proven experience architecting agentic systems with planning, tool use, orchestration, and multi-agent coordination Deep hands-on experience with agent frameworks such as LangGraph, LangChain, LlamaIndex, AutoGen, or CrewAI Strong command of agent state management, tool/function calling, and standards such as the Model Context Protocol (MCP) Demonstrated experience designing workflow orchestration systems, including DAG or state-machine execution, durable state, retry semantics, and idempotent step design Hands-on experience building connectors and integrations against enterprise systems and third-party APIs, including OAuth and service-account authentication, rate limiting, and permission-aware synchronization Production experience with background job and scheduling infrastructure (Celery, Airflow, Prefect, Temporal, or equivalent), including scheduled ingestion and failure recovery Hands-on experience with vector databases (Milvus, FAISS, Qdrant, pgvector) and lexical search (BM25, Elasticsearch) Expert command of retrieval techniques, including dense, sparse, hybrid, and filtered search, plus reranking Advanced prompt engineering and grounding techniques for large language models Track record of deploying, scaling, and optimizing production-ready agentic AI pipelines Strong grasp of RAG and agent evaluation methodologies, with the ability to define quality standards ​