AI Engineer

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. The starting salary for this role ranges from $100k – $150k per year, commensurate with experience.

Our team is tackling critical challenges in electric power system operations using Artificial Intelligence.

• 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 asLangChain, Lang Graph and related agentic libraries.
• Implement and optimizeretrieval-augmented generation (RAG) pipelines ensuring agents can access and incorporate external knowledge sources for grounded, accurate responses.
• Fine-tune and prompt-engineer
  
  LLMsfor task-specific reasoning, planning and dynamic adaptation.
• Lead the development of 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 of 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,…