AI Engineer

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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.

• 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
  

• 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
  

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