Qureos Mentor

Arcus Search

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 This is a contract role - Remote - Upto 30,000 AED/ month
 
 
 
 
 
 
 
 
 Senior LLM / AI Engineer &ndash; Agentic AI &amp; Platform Engineering
 
 
 
 
 
 
 Role Summary
 
 
 
 We are building an AI-powered enterprise platform that uses agentic AI to transform unstructured domain data into structured, actionable intelligence. As a Senior LLM / AI Engineer, you will design and build the core AI orchestration layer: multi-agent workflows, retrieval-augmented generation pipelines, tool-calling infrastructure, and the deterministic reasoning systems that make LLM outputs reliable enough for production use.
 
 
 
 
 
 This is not a research role. You will build production application services that real users depend on. You need to be equally strong in LLM engineering (prompting, structured outputs, evaluation) and software engineering (APIs, databases, deployment). We are looking for someone who can architect an agentic system from scratch, implement it in FastAPI, evaluate it rigorously, and operate it in production.
 
 
 
 
 
 
 Key Responsib
 
 ilities
 
 
 
 
 
 Agentic AI &amp; Multi-Agent Orches
 
 tration
 
 
 
 
 <ul>
 <li>
 Design and implement multi-agent systems where specialised agents collaborate on complex tasks: planning agents, execution agents, validation agents, and human-in-the-loop review steps
 </li>
 </ul>
 
 
 
 <ul>
 <li>
 Build agentic workflows using LangGraph, LangChain, or custom state machines with clear state management, conditional routing, retry logic, and graceful failure handling
 </li>
 </ul>
 
 
 
 <ul>
 <li>
 Implement tool-calling infrastructure: define tool schemas, manage tool registries, handle tool execution with timeouts and error recovery
 </li>
 </ul>
 
 
 
 <ul>
 <li>
 Design context management strategies for long-running agent sessions: conversation memory, working memory, context window optimisation, token budget management, and multi-turn state t
 
 racking
 
 </li>
 </ul>
 
 
 
 
 
 RAG &amp; Knowledge R
 
 etrieval
 
 
 
 
 <ul>
 <li>
 Build and optimise retrieval-augmented generation (RAG) pipelines: document chunking strategies, embedding model selection, vector store management (FAISS, Qdrant, pgvector, Pinecone), and hybrid search
 </li>
 <li>
 Implement advanced RAG patterns: multi-hop reasoning, reranking, query decomposition, self-querying retrieval, and citation grounding
 </li>
 <li>
 Design and maintain knowledge indexing pipelines that ingest, transform, and index domain-specific data
 
 at scale
 
 </li>
 </ul>
 
 
 
 
 
 Deterministic Workflows &amp; Structur
 
 ed Output
 
 <ul>
 <li>
 Design deterministic workflows that guarantee consistent, reliable outputsfrom LLMs
 </li>
 <li>
 Implement comprehensive input/output validation using Pydantic models, JSON Schema constraints, and structured output parsing
 </li>
 <li>
 Build hybrid pipelines combining deterministic business logic with LLM-powered
 
 reasoning
 
 </li>
 </ul>
 
 
 
 
 
 Evaluation, Testing &amp; Op
 
 timisation
 
 <ul>
 <li>
 Build evaluation frameworks for LLM-powered features: automated test suites, regression benchmarks, and continuous production monitoring
 </li>
 <li>
 Implement token optimisation strategies: prompt compression, caching, response streaming, batching, and model selection
 </li>
 <li>
 Design A/B testing infrastructure for prompt variants, model versions, and pipeline conf
 
 igurations
 
 </li>
 </ul>
 
 
 
 
 
 Platform &amp; Application
 
 Development
 
 <ul>
 <li>
 Build production APIs using FastAPI: endpoint design, async handlers, WebSocket/SSE for streaming responses, and graceful degradation
 </li>
 <li>
 Design and implement MCP (Model Context Protocol) server setups for standardised tool integration
 </li>
 <li>
 Work with PostgreSQL, Redis, and Elasticsearch for application state, caching, and search
 </li>
 <li>
 Containerise AI services with Docker and collaborate with DevOps on Kubernetes deployment
 </li>
 </ul>
 
 and scaling
 
 
 
 
 
 
 Required Qualifications
 
 
 <ul>
 <li>
 4+ years of software engineering experience, with at least 2 years focused on LLM-powered application developmentin production
 </li>
 <li>
 Strong proficiency in Python; experience with FastAPI or similar async frameworks required
 </li>
 <li>
 Deep hands-on experience with agentic AI frameworks: LangGraph, LangChain, or custom agent architectures
 </li>
 <li>
 Proven experience building RAG systems: embedding pipelines, vector databases, retrieval strategies, and multi-hop reasoning
 </li>
 <li>
 Strong understanding of LLM fundamentals: prompting, structured output, token management, and model selection trade-offs
 </li>
 <li>
 Working knowledge of PostgreSQL, Redis, and at least one vector database
 </li>
 <li>
 Experience with evaluation and testing of LLM outputs in production environments
 </li>
 <li>
 Solid understanding of REST APIs, WebSockets, and API lifecycle management
 </li>
 </ul>
 
 
 
 
 
 Preferred Experience
 
 
 <ul>
 <li>
 Experience with MCP architecture: building MCP servers and integrating external systems
 </li>
 </ul>
 
 as agent tools
 
 <ul>
 <li>
 Familiarity with parameter-efficient fine-tuning methods (LoRA, QLoRA)
 </li>
 <li>
 Experience building multi-tenant AI applications with organisation-
 </li>
 <li>
 level isolation
 </li>
 <li>
 LLM observability experience: tracing, token usage tracking, latency profiling
 </li>
 <li>
 Familiarity with Elasticsearch and hybrid search(BM25 + vector)
 </li>
 <li>
 Cloud deployment experience (AWS preferred): EKS,S3, SQS, Lambda
 </li>
 <li>
 Contributions to open-source AI/LLM projects or published work in NLP or agentic AI
 </li>
 </ul>
</div>

Senior AI Engineer

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Senior AI Engineer