Product AI

На данный момент Product AI Nuclear Consulting.

Мои компетенции и опыт

AI Transformation Lead — Nuclear Industry

Led the design and implementation of AI-enabled solutions across the nuclear-industry lifecycle, including uranium mining and processing, nuclear fuel-cycle operations, nuclear facility management, radioactive-waste management, and decommissioning.

Managed initiatives end to end: from business-problem identification, use-case assessment, and architecture design to pilot delivery, validation, production rollout, and operational adoption. Ensured that AI solutions deployed in highly regulated environments met requirements for cybersecurity, traceability, human oversight, information protection, and regulatory compliance.

Key Responsibilities

• Developed an AI transformation strategy and implementation roadmap covering nuclear engineering, operations, safety, project management, and corporate functions.

• Built and managed a portfolio of AI initiatives, prioritizing use cases according to business value, data readiness, technical feasibility, implementation complexity, and regulatory risk.

• Translated engineering, operational, and regulatory requirements into product specifications, system requirements, delivery roadmaps, KPIs, acceptance criteria, and investment proposals.

• Designed secure hybrid AI architectures combining local LLMs, external AI services, APIs, enterprise databases, document repositories, and retrieval-augmented generation systems.

• Defined AI-system architecture, including data flows, integrations, access roles, trust boundaries, information-leakage controls, logging, monitoring, auditability, and human-approval mechanisms.

• Established controlled AI access to engineering documentation, regulatory requirements, technical reports, project data, correspondence, and corporate knowledge bases.

• Led the development of AI-powered knowledge-search and document-intelligence solutions for technical documentation, regulations, engineering reports, and project correspondence.

• Implemented AI assistants for requirements analysis, document comparison, classification, information extraction, regulatory review, and preparation of draft technical materials.

• Applied predictive analytics to project-portfolio management, equipment reliability, maintenance planning, operational-risk assessment, and management decision support.

• Evaluated and designed computer-vision use cases for industrial inspection, equipment monitoring, anomaly detection, and occupational safety.

• Conducted architectural and cybersecurity reviews of AI solutions, covering prompt injection, data leakage, excessive privileges, unsafe integrations, external-service risks, and uncontrolled use of model-generated outputs.

• Introduced human-in-the-loop validation, source attribution, response-quality evaluation, controlled testing, and phased production deployment.

• Coordinated cross-functional teams of nuclear-industry experts, IT architects, data scientists, software developers, cybersecurity specialists, legal advisers, and compliance teams.

• Managed product backlogs, external contractors, pilot programmes, delivery risks, acceptance criteria, and the transition of AI solutions from prototype to production.

• Presented implementation progress, architectural decisions, risks, budgets, and investment recommendations to executive stakeholders.

Key Achievements

• Established a scalable governance and delivery framework for introducing AI into safety-critical and highly regulated nuclear-industry processes.

• Created a practical transition model from isolated AI experiments to secure, controlled, and operationally supported production systems.

• Reduced manual effort associated with processing engineering documentation, regulatory requirements, technical reports, and project correspondence.

• Accelerated access to engineering and regulatory knowledge through AI-powered search, document analysis, and knowledge-management tools.

• Improved the consistency, transparency, auditability, and traceability of technical and management decisions.

• Introduced structured controls for AI-related cybersecurity and operational risks, including human approval, source verification, access restrictions, logging, and response-quality evaluation.

• Enabled effective collaboration between nuclear experts, business stakeholders, IT teams, cybersecurity functions, and external development partners.

• Developed a reusable hybrid AI architecture suitable for multiple nuclear-industry use cases without compromising control over sensitive technical and corporate information.

• Created a prioritized portfolio of AI initiatives linked to measurable business value, operational efficiency, implementation feasibility, and regulatory risk.

• Strengthened organizational capability to assess, design, validate, and deploy AI solutions in mission-critical nuclear environments.