Artificial Intelligence Consultant

• Practical understanding of transformer architectures and how modern

LLMs work.

• Hands-on experience with

MCP (Model Context Protocol).

• Agentic orchestration using frameworks such as Semantic Kernel, LangChain/LangGraph, Microsoft Copilot Studio, OpenAI/Anthropic tool-calling patterns, or equivalent.
• Prompt engineering: systematic prompt design, few-shot learning, chain-of-thought, and prompt optimization for foundation models.
• Vector

databases (e.g., Pinecone, FAISS, Weaviate, Chroma) and RAG architecture patterns.

• LLM evaluation methods, including

LLM-as-a-judge, automated benchmarking, and regression testing of model outputs.

• Post-deployment model maintenance: observability, monitoring, cost optimization, and drift detection.
• Developing and implementing governance frameworks and controls for responsible AI.

Experimentation and measurement:

• A/B testing, KPI instrumentation, and operational analytics.
• Strong Python proficiency; working knowledge of Java or another enterprise language.
• Object-oriented design techniques and principles.
• Cloud platforms (Azure, AWS, or GCP) and cloud-native development patterns.
• CI/CD pipelines and DevOps tools: Git, GitHub, Jenkins, ADO Pipelines, or equivalent.
• RESTful API design and integration patterns (JSON, microservices).
• Agile methodologies such as Scrum.
• Microsoft Office tools (Project, Excel, Word, PowerPoint).
• Familiarity with AI-assisted development tools (GitHub Copilot, Cursor, Claude Code) is a plus