Senior AI Engineer, IT Solutions 1

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, "drift," and hallucination rates in production environments.
• Ensure AI solutions adhere to corporate data governance, security, and ethical AI principles.