AI Engineer

About This Role

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.

7. Production Operations and Optimization

• Manage and improve deployment pipelines using Docker, Kubernetes, and CI/CD automation.
• Monitor system health, latency, and cost metrics post-launch.
• Implement observability dashboards using LangFuse, Prometheus, and custom analytics.
• Optimize performance through LLM caching, query batching, and result streaming.
• Support incident response for agent failures, data quality issues, and system degradation.

REQUIRED QUALIFICATIONS

Education

Master's degree or PhD in AI, Data Science, Computer Science, Machine Learning, or a similar field.

Programming Expertise

• 7+ years of professional Python development.
• Comfortable with async/await, type hints, and modern Python idioms.
• Hands-on experience with FastAPI, Flask, or similar frameworks.
• Full-stack Python development experience, including backend API design, service integration, and frontend coordination.
• React experience for building production web interfaces, dashboards, and data-driven user workflows.
• Experience integrating React frontends with Python/FastAPI backend services and REST APIs.
• Git proficiency and CI/CD pipeline experience.

Agentic AI Frameworks

• Production experience with LangGraph or equivalent state machine frameworks.
• Deep expertise in LangChain, including tools, chains, agents, and RAG.
• Familiarity with LangFuse for agent observability.
• Knowledge of advanced agentic patterns, including reflection, planning, and tool-use optimization.

LLM and Prompt Engineering

• At least 2+ years of prompt engineering experience, including few-shot learning, chain-of-thought, and RAG.
• Understanding of different LLM architectures and their trade-offs, including GPT-4, Claude, Llama, and similar models.
• Experience with smaller language models for cost optimization.
• Ability to evaluate and select models for specific use cases.

Data Infrastructure and SQL

• Advanced SQL knowledge, including query optimization, window functions, and complex joins.
• Experience with Snowflake or similar cloud data warehouses such as BigQuery or Redshift.
• Graph database experience with Neo4j, including Cypher queries and relationship modeling.
• Vector database experience with ChromaDB, Pinecone, or Weaviate.
• Data modeling and schema design expertise.

Cloud Platforms

• Production experience with Azure AI services or AWS AI stack, or both.
• Comfort with containers such as Docker and orchestration platforms such as Kubernetes.
• Familiarity with cloud storage such as Azure Blob Storage and S3.
• Infrastructure as Code experience using Terraform, CloudFormation, ARM templates, or similar tools.

Software Engineering Practices

• Architecture design and trade-off analysis.
• System design for scalability, reliability, and maintainability.
• Testing strategies including unit, integration, and end-to-end testing.
• Observability and monitoring design.
• Security awareness including input validation, injection prevention, and access controls.

PREFERRED QUALIFICATIONS

Research and Innovation

• Familiarity with academic agentic AI research papers.
• Contributions to AI/ML open-source projects.
• Participation in AI communities, research communities, or technical forums.

Domain Experience

• Manufacturing, supply chain, pharmaceutical, or quality control domain knowledge.
• Experience with anomaly detection or time-series analysis.
• Knowledge of data quality frameworks and validation patterns.

Advanced Skills

• Machine learning model development and evaluation.
• NLP and embeddings experience, including Hugging Face and sentence-transformers.
• Real-time data processing with Kafka, Flink, or similar technologies.
• Database performance tuning and query optimization.

WHAT WE ARE BUILDING

The AI Engineer will help build a sophisticated multi-agent agentic system for a large pharmaceutical industry environment.

System Architecture

The system includes 10 major components across 12 execution phases:

1. Input Processing: Session management and request validation.

2. Memory and State: LangGraph-based state store with checkpoint persistence.

3. Planning: Task decomposition and dependency management.

4. Data Agents: SQL query generation, document retrieval, and knowledge graph traversal.

5. Human-in-the-Loop: Data quality validation before synthesis.

6. Synthesis: Multi-source data consolidation and context building.

7. Analysis Planning: Task breakdown for visualization and ML analysis.

8. Visualization Agent: Chart and dashboard generation.

9. ML Analysis Agent: Anomaly detection and predictive insights.

10. Report Assembly: Final insight synthesis and formatting.

11. Checkpoint Persistence: Session state archival.

12. Suggested Questions: Dynamic follow-up generation.

Technical Stack

• Core: Python 3.10+, FastAPI, Pydantic, React.
• Frontend: React, JavaScript or TypeScript, data dashboards, API-driven user interfaces.
• Agentic Frameworks: LangGraph, LangChain, LangFuse, Deep Agents.
• Data: Snowflake, Neo4j, ChromaDB, PostgreSQL for checkpoints, Redis for caching.
• Cloud: Azure AI or AWS Bedrock, Blob Storage, and compute services.
• DevOps: Docker, Kubernetes, GitHub Actions or GitLab CI.
• Monitoring: LangFuse, Prometheus, and structured logging.

Business Context

• Operations in a large pharmaceutical industry environment, including batch processing, quality control, and supply chain visibility.
• Structured and unstructured data analysis, combining SQL queries with document insights.
• Real-time anomaly detection and proactive alerting.
• User-facing intelligent assistant capabilities for manufacturing teams asking complex questions and receiving synthesized answers.

WHY THIS ROLE IS UNIQUE

• Technical breadth and depth: Work across systems thinking, hands-on coding, Python, React, full-stack implementation, and agentic AI.
• Emerging technology: Work with cutting-edge agentic frameworks before they become mainstream.
• Real-world impact: Build systems that support manufacturing decisions in a large pharmaceutical industry environment.
• Collaborative engineering: Work closely with AI engineers, data teams, and domain specialists on production-grade systems.
• Innovation culture: Help establish practical best practices in agentic AI systems for pharmaceutical operations.

COMPETENCIES AND MINDSET

Technical Mindset

• Systems thinker: Understand how components interact and anticipate failure modes.
• Pragmatist: Choose appropriate trade-offs between simplicity, performance, and extensibility.
• Continuous learner: Comfortable keeping pace with the rapidly evolving agentic AI landscape.
• Quality-first: Prioritize reliability and robustness for production systems.

Interpersonal Skills

• Communicator: Explain complex agentic architectures to non-technical stakeholders.
• Collaborator: Work cross-functionally with data engineers, ML researchers, manufacturing teams, and business stakeholders.
• Ownership: Take accountability for implementation quality and system outcomes.
• Knowledge sharer: Contribute to documentation, reviews, and engineering standards.

Job Type: Full-time

Pay: From QAR16,000.00 per month

Work Location: In person