AI Engineer

AI Engineer

Industry: Large pharmaceutical industry

Location:

Doha, Qatar

Employment Type:

Full-time, on-site in office

Company:
Intelligence Experts, Doha, Qatar

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 Lang Graph, Lang Chain, and Lang Fuse, 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.

• 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.

• Write production-quality Python code for critical agent components.
• Build Lang Graph 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 Lang Fuse.
• Build and maintain full-stack features using Python backend services and React frontend interfaces.

• Support adoption and optimization of Lang Graph, Lang Chain, Lang Fuse, and Deep Agents.
• Work with Lang Graph for multi-agent state machines, graph-based workflows, and parallel execution patterns.
• Work with Lang Chain for tool definitions, chains, retrieval-augmented generation, and agent workflows.
• Work with Lang Fuse 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.

• Design and implement integrations with Snowflake, Neo4j, Chroma
  
  DB, 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 Chroma
  
  DB, 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.

• 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.

• 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.

• Manage and improve deployment pipelines using Docker, Kubernetes, and CI/CD automation.
• Monitor system health,…