AI Agent Architect - Industrial AI Systems - Halliburton

We are looking for the right people — people who want to innovate, achieve, grow and lead. We attract and retain the best talent by investing in our employees and empowering them to develop themselves and their careers. Experience the challenges, rewards and opportunity of working for one of the world’s largest providers of products and services to the global energy industry.

Job Description:

We are seeking a hands-on and qualified AI Agent Architect to design and deploy advanced Agentic AI systems—comprising task-specific autonomous tools governed by a master agent—to support complex technical decision-making in industrial environments.

This is a high-impact individual contributor role for someone who can independently deliver full-stack intelligent agents that interpret natural language queries and generate precise, context-aware outputs by interacting with structured and unstructured data, APIs, and analytical engines.

Key Responsibilities:

• 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.

Required Skills and Experience:

• 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.

Knowledge, Skills, and Abilities

Nice to Have:

• 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