AI Engineer

Base Pay Range

$/yr - $/yr

The Open Access Technology International (OATI) is seeking highly motivated individuals to join our team of AI Engineers focused on power systems. This is a fantastic opportunity to gain practical experience working on real‑world AI applications alongside leading experts in the energy sector.

• Conduct research and development on specific AI problems critical to power system operations, leveraging OATI's vast datasets spanning decades (e.g., forecasting load, wholesale electricity market prices, renewable energy generation, optimizing grid reliability, resiliency, and stability, signature analysis and anomaly detection).
• Design, build, and deploy agentic AI systems using frameworks such as Lang Chain, Lang Graph and related agentic libraries.
• Implement and optimize retrieval‑augmented generation (RAG) pipelines ensuring agents can access and incorporate external knowledge sources for grounded, accurate responses.
• Fine‑tune and prompt‑engineer LLMs for task‑specific reasoning, planning and dynamic adaptation.
• Lead the development of an enterprise‑grade AI platform that integrates advanced generative AI and LLM technologies.
• Design, build and fine‑tune large foundation AI models (e.g., LLMs, multimodal models) for the energy domain.
• Develop and deploy agentic AI systems capable of autonomous decision‑making and multi‑agent collaboration.
• Build and optimize neural network models tailored to use cases in the energy/power systems industry ensuring high performance and scalability.
• Implement and standardize Model Context Protocol based communication to ensure consistent context management across AI models and agents.
• Establish and enforce best practices for MLOps, model monitoring and observability to ensure robust, scalable and maintainable AI solutions.
• Develop and implement machine learning models using popular frameworks (e.g., Tensor Flow, PyTorch) to analyze and extract meaningful insights from power system data.
• Participate in the full research cycle, including literature review, data exploration, experimentation, analysis and presentation of findings.
• Collaborate effectively with other researchers, engineers and data scientists.
• Contribute to the development and documentation of technical code.

Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field;
PhD preferred.

• 3+ years building and deploying AI‑powered systems using LLMs, RAG and agentic architectures.
• Solid Python programming skills and experience with modern AI/ML libraries.
• Advanced knowledge of LLMs, including fine‑tuning, prompt engineering, and evaluation.
• Proven experience implementing RAG systems and integrating vector databases or external knowledge stores.
• Proven experience in building and fine‑tuning foundation models (e.g., LLMs, vision transformers, multimodal models) for different domains (such as energy, healthcare, finance, etc.).
• Past experience in implementing association rule mining algorithms to uncover patterns and relationships within large and diverse datasets.
• Past experience in building similarity search pipelines using vector representations to enable accurate ranking and retrieval based on multidimensional feature similarity.
• Deep knowledge of agentic AI frameworks and multi‑agent system design.
• Proven ability to design and implement multi‑agent systems and agent‑to‑agent communication.
• Strong background in neural network architectures, including transformers and other models.
• Strong background in advanced mathematics and statistics for model optimization and validation.
• Familiarity with Model Context Protocol and best practices for managing AI model context and state.
• Proficiency in Python and relevant AI/ML frameworks such as Tensor Flow or PyTorch.
• Excellent problem‑solving skills and ability to communicate complex AI concepts to technical and non‑technical stakeholders.
• Strong foundation in machine learning concepts, including algorithms (e.g., deep learning, reinforcement learning) and statistical methods.
• Experience with AI system security, compliance, and ethical AI considerations.
• Knowledge of…