Senior AI Engineer
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Key skills for this role
About the Role
Arcus Search is seeking a Senior AI Engineer to design and build the core AI orchestration layer for an enterprise platform. You will develop multi-agent workflows, RAG pipelines, and production APIs using FastAPI.
Key Skills for This Role
Responsibilities
- Design and implement multi agent systems using LangGraph, LangChain, or custom state machines
- Build and optimize RAG pipelines including document chunking, embedding, vector store management, and hybrid search
- Implement deterministic workflows and structured output using Pydantic and JSON Schema
- Build evaluation frameworks for LLM powered features including automated test suites and production monitoring
- Develop production APIs using FastAPI with async handlers, WebSocket/SSE for streaming
- Containerize AI services with Docker and collaborate on Kubernetes deployment
Requirements
- 4+ years of software engineering experience
- At least 2 years focused on LLM powered application development in production
- Strong proficiency in Python; experience with FastAPI or similar async frameworks
- Deep hands on experience with agentic AI frameworks: LangGraph, LangChain, or custom agent architectures
- Proven experience building RAG systems
- Strong understanding of LLM fundamentals
- Working knowledge of PostgreSQL, Redis, and at least one vector database
- Experience with evaluation and testing of LLM outputs in production
- Solid understanding of REST APIs, WebSockets, and API lifecycle management
Full Job Posting
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 deterministic reasoning systems.
- This is not a research role. You will build production application services that real users depend on.
Key Responsibilities
- Design and implement multi agent systems where specialised agents collaborate on complex tasks.
- Build agentic workflows using LangGraph, LangChain, or custom state machines with clear state management, conditional routing, retry logic, and graceful failure handling.
- Implement tool calling infrastructure: define tool schemas, manage tool registries, handle tool execution with timeouts and error recovery.
- Design context management strategies for long running agent sessions.
- Build and optimise retrieval augmented generation (RAG) pipelines: document chunking strategies, embedding model selection, vector store management, and hybrid search.
- Implement advanced RAG patterns: multi hop reasoning, reranking, query decomposition, self querying retrieval, and citation grounding.
- Design and maintain knowledge indexing pipelines that ingest, transform, and index domain specific data at scale.
- Design deterministic workflows that guarantee consistent, reliable outputs from LLMs.
- Implement comprehensive input/output validation using Pydantic models, JSON Schema constraints, and structured output parsing.
- Build evaluation frameworks for LLM powered features: automated test suites, regression benchmarks, and continuous production monitoring.
- Implement token optimisation strategies: prompt compression, caching, response streaming, batching, and model selection.
- Build production APIs using FastAPI: endpoint design, async handlers, WebSocket/SSE for streaming responses, and graceful degradation.
Required Qualifications
- 4+ years of software engineering experience, with at least 2 years focused on LLM powered application development in production.
- Strong proficiency in Python; experience with FastAPI or similar async frameworks required.
- Deep hands on experience with agentic AI frameworks: LangGraph, LangChain, or custom agent architectures.
- Proven experience building RAG systems: embedding pipelines, vector databases, retrieval strategies, and multi hop reasoning.
- Strong understanding of LLM fundamentals: prompting, structured output, token management, and model selection trade offs.
- Working knowledge of PostgreSQL, Redis, and at least one vector database.
- Experience with evaluation and testing of LLM outputs in production environments.
- Solid understanding of REST APIs, WebSockets, and API lifecycle management.
Preferred Experience
- Experience with MCP architecture: building MCP servers and integrating external systems as agent tools.
- Familiarity with parameter efficient fine tuning methods (LoRA, QLoRA).
- Experience building multi tenant AI applications with organisation level isolation.
- LLM observability experience: tracing, token usage tracking, latency profiling.
- Familiarity with Elasticsearch and hybrid search (BM25 + vector).
- Cloud deployment experience (AWS preferred): EKS, S3, SQS, Lambda.
- Contributions to open source AI/LLM projects or published work in NLP or agentic AI.
Compensation
- Up to 30,000 AED per month.
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