AI Prompt Engineer

Job Title: AI Prompt Engineer



Location: Remote
Job Type: Contract

Job Description:

The resource will support the design, development, and delivery of Agentic AI solutions by creating, optimizing, and managing AI prompts, agent workflows, and MCP-integrated tools. The role will focus on enabling intelligent AI agents capable of autonomous reasoning, task orchestration, tool utilization, and workflow execution across enterprise applications

Required Skills

• Experience with Large Language Models (OpenAI, Azure OpenAI, Anthropic, Gemini, or equivalent).
• Strong understanding of prompt engineering, AI agent frameworks, and conversational AI systems.
• Experience building Agentic AI applications using Semantic Kernel, LangChain, LangGraph, AutoGen, CrewAI, or similar frameworks.
• Hands-on experience with MCP servers, tool integration, API orchestration, and enterprise system connectivity.
• Proficiency in Python, TypeScript, or similar programming languages.
• Familiarity with RAG architectures, vector databases, embeddings, and knowledge retrieval systems.
• Understanding of cloud platforms such as Azure AI Foundry, Azure OpenAI, AWS Bedrock, or Google Vertex AI.
• Strong problem-solving, analytical, and collaboration skills.

Preferred Qualifications

• Experience building enterprise copilots, AI assistants, or autonomous agent ecosystems.
• Knowledge of AI governance, responsible AI, and security best practices.
• Experience integrating AI agents with healthcare, health records, or regulated industry systems

Key Responsibilities

• Design, develop, and optimize prompts for Large Language Models (LLMs) to improve accuracy, reliability, and business outcomes.
• Build and configure Agentic AI solutions that leverage planning, reasoning, memory, and multi-step task execution capabilities.
• Develop and integrate MCP (Model Context Protocol) tools, enabling AI agents to securely discover and interact with enterprise systems, APIs, and data sources.
• Design agent architectures, tool-calling frameworks, retrieval mechanisms, and context management strategies.
• Collaborate with product managers, architects, and engineering teams to translate business requirements into AI-driven solutions.
• Implement Retrieval-Augmented Generation (RAG), knowledge grounding, and context orchestration patterns.
• Define evaluation frameworks and prompt testing methodologies to measure agent performance, quality, and reliability.
• Ensure AI solutions adhere to security, compliance, governance, and responsible AI standards.
• Support deployment, monitoring, troubleshooting, and continuous improvement of AI agents and MCP-enabled workflows.
• Contribute to architecture reviews, technical design documentation, and engineering best practices for AI platforms.