Design, build, and optimize agentic AI solutions, including LLM prompts, agent workflows, MCP tools, tool-calling frameworks, RAG systems, and context orchestration. Develop evaluation methods, integrate enterprise systems and APIs, support deployment and monitoring, and ensure solutions meet security, governance, compliance, and responsible AI standards. The role is client-facing and involves collaboration across product, architecture, and engineering teams.
Job Description
AI Prompt Engineer / Agentic AI Engineer (3 ON, 3 Off)
Role Summary
- Role Type: Client-facing, hands-on engineering + solutioning (pre-sales through early delivery)
- Location: Major US hub (e.g., New York, Bay Area, Dallas, Chicago – flexible for the right candidate). Travel Expected.
- Level: Manager/Principal/Senior Principal
- Business Unit: (Enterprise AI Platforms & Agentic Solutions).
- Reporting to: Artificial Intelligence Leader, North America.
About Company:
Role summary
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.
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 AIagents 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.
Required Skill
- 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
All your information will be kept confidential according to EEO guidelines.
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