Description

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.

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 operations or engineering workflows.
• However, candidates with relevant AI system-building experience in other complex industries are encouraged to apply

Key Skills

Education

Any Graduate