Applied AI / GenAI Engineer
United States
About XCIS AI
XCIS AI is a nuclear-focused data and AI consulting firm supporting plant operations, work management, data modernization, automation, and governed AI in highly regulated environments. We work at the intersection of nuclear operations, enterprise data platforms, AI-enabled decision support, and workflow modernization.
Work Authorization / Regulatory Note
Applicants must be authorized to work in the United States. Certain roles may require eligibility to support customer, export-control, security, or regulatory requirements.
Location
U.S.-based. Remote / hybrid, with periodic travel to customer or project locations as required.
Role Summary
We are seeking an Applied AI / GenAI Engineer to design and implement AI-enabled decision-support capabilities across classification, similarity detection, prioritization, retrieval, and controlled draft generation workflows. This role will bridge traditional machine learning, retrieval-based architectures, and enterprise-ready generative AI patterns.
Key Responsibilities
• Develop and evaluate AI / ML solutions for classification, duplicate detection, prioritization, and recommendation workflows
• Design and implement RAG pipelines, retrieval orchestration, prompt logic, and controlled generative workflows
• Build evaluation frameworks for recommendation quality, relevance, accuracy, and traceability
• Work with data engineers to prepare model inputs, features, embeddings, and retrieval indexes
• Support prompt versioning, model versioning, testing, and deployment practices
• Tune AI workflows for reliability, auditability, and human-in-the-loop operation
• Collaborate with business stakeholders to translate workflow problems into practical AI patterns
• Support pilot deployment, validation, and ongoing optimization
Required Qualifications
• 4+ years of experience in applied machine learning, AI engineering, or GenAI development
• Strong Python skills
• Experience with classification models, similarity search, ranking, or recommendation systems
• Experience with LLMs, prompt design, RAG, and retrieval-based workflows
• Familiarity with ML lifecycle tooling such as MLflow or equivalent
• Experience building production-oriented AI solutions rather than experimental prototypes
• Strong communication skills and ability to explain AI behavior to technical and business teams
Preferred Qualifications
• Experience with Azure AI Foundry, Azure OpenAI, Databricks ML, or vector search platforms
• Experience with industrial, work management, maintenance, or nuclear use cases
• Experience with explainability, evaluation, and governed AI deployment in regulated settings
• Experience with semantic retrieval over technical documents and enterprise repositories