Senior AI Engineer, IT Solutions 1

Req ID: 132986 Remote Position: Hybrid Region: Americas Country: USA General Overview Functional Area: Information Technology (ITM) Career Stream: IT Solutions Role: Technical Lead (TEL) Job Title: Technical Lead, IT Solutions Job Code: TL-ITM-SOLN Job Level: Level 10 Direct/Indirect Indicator: Indirect Summary We are seeking a highly motivated and technically proficient AI Engineer to join our growing Data & Analytics team. In this role, you will be a key liaison between business stakeholders and the technical AI team, translating complex business challenges into scalable artificial intelligence and machine learning solutions. You will be responsible for defining technical requirements, designing AI architectures (including Generative AI and RAG patterns), and collaborating with the Data Center of Excellence to deliver high-quality, production-ready AI tools that drive innovation and operational efficiency across the organization. Detailed Description AI Solution Scoping & Requirements: + Elicit and document technical requirements for AI and Machine Learning projects through workshops and deep dives with stakeholders across various departments. + Define the technical feasibility of proposed AI use cases, identifying appropriate model architectures (LLMs, SLMs, or traditional ML) and success metrics (Accuracy, F1-score, Perplexity, etc.). + Analyze existing business processes to identify automation opportunities and areas where Generative AI can provide a competitive advantage. Data Engineering & AI Pipeline Design: + Work with stakeholders to identify and prepare high-quality datasets for model training, fine-tuning, and grounding. + Design and implement data ingestion pipelines for vector databases, ensuring data integrity and optimal embedding strategies for Retrieval-Augmented Generation (RAG). + Collaborate with data engineers to ensure scalable, secure, and compliant data flows between enterprise systems and AI models. Model Development & Orchestration: + Develop, test, and refine AI prompts and orchestration workflows using frameworks like LangChain, LlamaIndex, or Semantic Kernel. + Evaluate and select appropriate foundation models (OpenAI, Anthropic, Llama, etc.) based on performance, cost, and latency requirements. + Translate business logic into technical specifications for API integrations, model endpoints, and user interfaces. MLOps, Deployment & Monitoring: + Implement MLOps best practices to ensure the continuous integration and deployment (CI/CD) of AI models. + Establish monitoring frameworks to track model performance,

Created: 2026-03-07