AI Engineer

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