Architect and develop a multi-agent AI framework where autonomous agents coordinate to solve domain-specific technical queries.

Leverage LLMs, NLP, and tool-based reasoning to automate data extraction, analysis, and insight generation.

Build agents capable of integrating with engineering tools, simulators, databases, and knowledge sources.

Collaborate with domain experts to align agent behavior with technical expectations and constraints.

Implement safeguards to ensure accuracy, traceability, and reliability of AI-generated outputs.

Continuously optimize prompting, agent orchestration, and performance under real-world conditions.

Demonstrated expertise in building Agentic AI architectures, using frameworks like LangChain, AutoGen, CrewAI, or custom stacks.

Strong foundation in LLM-based NLP, prompt engineering, and context-aware reasoning.

Advanced Python programming and experience deploying AI workflows in cloud or containerized environments.

Ability to work with APIs, data models, and external toolchains across complex systems.

Comfortable operating independently with minimal supervision in a cross-functional environment.

Ready for a long term business trip to Kuwait for first 6-12 months

Exposure to industrial domains such as energy, manufacturing, or heavy engineering.

Understanding of vector databases, knowledge graphs, and retrieval-augmented generation.

Familiarity with Azure or AWS development environments.

Certifications:

Azure AI Engineer or Azure Data Engineer certification is a plus.

AWS experience is nice to have, but not required.

Industry Experience:

Oil and gas domain experience is a strong advantage, especially familiarity with digital operations or engineering workflows.

However, candidates with relevant AI system-building experience in other complex industries are encouraged to apply.