AI Engineer

Job description AI Engineer Industry: Large pharmaceutical industry Location: Doha, Qatar Employment Type: Full-time, on-site in office Company: Intelligence Experts, Doha, Qatar POSITION OVERVIEW We are seeking an exceptional AI Engineer to design, build, and evolve sophisticated multi-agent AI systems for a large pharmaceutical industry environment. This role focuses on production-grade agentic AI, manufacturing intelligence, quality control, and operational analytics across complex pharmaceutical operations. The AI Engineer will work hands-on with modern agentic frameworks including LangGraph, LangChain, and LangFuse, while integrating enterprise data infrastructure such as Azure/AWS, Snowflake, Neo4j, vector databases, and full-stack Python and React applications. Impact: Direct influence on systems that optimize batch processing, enable real-time anomaly detection, and synthesize insights from billions of data points across pharmaceutical manufacturing operations. KEY RESPONSIBILITIES 1. Agentic Architecture and Design • Design and evolve the core agentic architecture supporting multi-agent workflows, including planning, data fetching, synthesis, analysis, and reporting. • Define state management patterns, checkpoint strategies, and memory systems for long-running agent conversations. • Architect Human-in-the-Loop integration patterns for quality assurance and risk mitigation. • Establish best practices for agent composition, tool design, and inter-agent communication. • Create technical roadmaps that balance innovation with production stability. 2. Hands-On Development • Write production-quality Python code for critical agent components. • Build LangGraph state management, checkpoint services, and session handling. • Develop data agents for SQL query generation, semantic validation, document parsing, embedding, and knowledge graph traversal. • Develop orchestration agents for task planning, dependency management, and workflow coordination. • Build analysis agents for visualization generation, anomaly detection, and ML-driven insights. • Implement sophisticated prompt engineering for SQL generation, synthesis, and reasoning tasks. • Build robust validation pipelines, including SQL injection prevention, schema validation, and result sanity checks. • Develop real-time monitoring and observability instrumentation using LangFuse. • Build and maintain full-stack features using Python backend services and React frontend interfaces. 3. Framework and Stack Expertise • Support adoption and optimization of LangGraph, LangChain, LangFuse, and Deep Agents. • Work with LangGraph for multi-agent state machines, graph-based workflows, and parallel execution patterns. • Work with LangChain for tool definitions, chains, retrieval-augmented generation, and agent workflows. • Work with LangFuse for agent tracing, observability, and performance analytics. • Apply advanced agentic patterns including reflection, planning, and tool-use optimization. • Maintain deep knowledge of emerging agentic frameworks and contribute to technology evaluation. • Guide technology choices, including when to use LLMs vs. SLMs, caching strategies, and cost optimization. 4. Cloud and Data Infrastructure • Design and implement integrations with Snowflake, Neo4j, ChromaDB, Azure AI services, and AWS AI stack. • Work with Snowflake for query optimization, cost control, and schema design. • Work with Neo4j for semantic search, relationship modeling, and document discovery. • Work with vector stores such as ChromaDB, Pinecone, or Weaviate for embedding management, semantic indexing, and RAG optimization. • Architect file system abstraction layers for Azure Blob Storage, S3, and local storage. • Design and optimize database schemas for checkpoint persistence and result tracking. • Implement connection pooling, caching strategies, and performance optimization. 5. Collaboration and Knowledge Sharing • Collaborate with AI engineers, data engineers, ML researchers, manufacturing teams, and business stakeholders. • Conduct code reviews with attention to architectural consistency and quality. • Pair program on complex implementations, including prompt engineering, agent coordination, and validation. • Share knowledge through documentation, architecture decision records, and technical discussions. • Contribute to engineering practices, including testing strategies, deployment procedures, and incident response. 6. Quality, Testing, and Reliability • Design comprehensive validation frameworks. • Build unit tests for agent components with mocked LLM responses. • Build integration tests for multi-agent workflows. • Build end-to-end tests simulating real manufacturing queries. • Implement safety guardrails including SQL injection prevention, query cost estimation, and anomaly detection. • Establish error handling and graceful degradation patterns. • Drive observability through structured logging, distributed tracing, and performance dashboards.