Agentic AI Engineer – Workflow Orchestration & Prompt Engineer

Experis (Austria); Charlotte (NC); On-site; USD 110,000 - 160,000; Full time

Job summary

Agentic AI Engineer role in Charlotte, NC onsite focused on workflow orchestration, prompt/context engineering, and reliable operational execution.

Responsibilities include designing multi-step prompt workflows, translating business problems into structured AI designs, and maintaining reusable prompt libraries and orchestration patterns. The role emphasizes tooling, context management, logging analysis, and governance for reliable AI outcomes.

Qualifications

• Experience designing, implementing, and optimizing multi-step LLM, generative AI, and agentic workflows.
• Strong problem-solving ability to translate complex business challenges into scalable AI solutions.
• Expertise in prompt engineering, human-in-the-loop workflows, and AI performance optimization.
• Ability to troubleshoot and diagnose issues across prompts, agents, context management, integrations, and workflow execution.
• Proficiency in Python, APIs, JSON, Markdown, and working with structured and unstructured data.

Responsibilities

• Design, develop, and optimize multi-step prompt workflows that support AI agent activities such as reasoning, decision support, content generation, analysis, and task execution.
• Translate business problems into structured AI workflow designs by breaking complex tasks into logical stages and execution paths.
• Design and maintain reusable prompt libraries, workflow templates, and orchestration patterns, including version control, evaluation criteria, change tracking, and lifecycle management.
• Define workflow architecture covering prompts, context requirements, tool interactions, orchestration logic, handoffs, dependencies, and output requirements.
• Determine how information, memory, context, and intermediate outputs are managed throughout execution.
• Analyze execution logs, model outputs, and intermediate agent responses to identify root causes of failures, performance degradation, and inconsistent results.
• Troubleshoot issues across prompt design, context management, agent behavior, tool integrations, data pipelines, file structures, APIs, and execution environments.
• Evaluate workflow effectiveness using qualitative and quantitative measures; implement improvements to increase reliability, accuracy, efficiency, and user outcomes.
• Improve context engineering strategies to optimize retrieval, grounding, memory management, and response quality.
• Develop automation, scripts, and utilities to support workflow development, experimentation, testing, evaluation, monitoring, and operational efficiency.
• Perform data inspection, transformation, validation, and analysis to support workflow development and troubleshooting.
• Contribute to AI governance, risk management, quality assurance, and responsible AI practices by ensuring workflows operate within established standards and controls.
• Stay current on emerging AI technologies, agent frameworks, orchestration platforms, prompt engineering techniques, and industry best practices.

Skills

LLM workflows

Agent orchestration

Prompt engineering

Python

JSON

Markdown

Problem solving

Tools

GitHub Copilot

Claude Code

Cursor

APIs

JSON

Markdown

Job description

Agentic AI Engineer role in Charlotte, NC (onsite) focused on workflow orchestration, prompt/context engineering, and reliable operational execution.

Responsibilities

• Design, develop, and optimize multi-step prompt workflows that support AI agent activities such as reasoning, decision support, content generation, analysis, and task execution.
• Translate business problems into structured AI workflow designs by breaking complex tasks into logical stages and execution paths.
• Design and maintain reusable prompt libraries, workflow templates, and orchestration patterns, including standardized templates, version control, evaluation criteria, change tracking, and lifecycle management.
• Define workflow architecture covering prompts, context requirements, tool interactions, orchestration logic, handoffs, dependencies, and output requirements.
• Determine how information, memory, context, and intermediate outputs are managed throughout execution.
• Analyze execution logs, model outputs, and intermediate agent responses to identify root causes of failures, performance degradation, and inconsistent results.
• Troubleshoot issues across prompt design, context management, agent behavior, tool integrations, data pipelines, file structures, APIs, and execution environments.
• Evaluate workflow effectiveness using qualitative and quantitative measures; implement improvements to increase reliability, accuracy, efficiency, and user outcomes.
• Improve context engineering strategies to optimize retrieval, grounding, memory management, and response quality.
• Develop automation, scripts, and utilities to support workflow development, experimentation, testing, evaluation, monitoring, and operational efficiency.
• Perform data inspection, transformation, validation, and analysis to support workflow development and troubleshooting.
• Contribute to AI governance, risk management, quality assurance, and responsible AI practices by ensuring workflows operate within established standards and controls.
• Stay current on emerging AI technologies, agent frameworks, orchestration platforms, prompt engineering techniques, and industry best practices.

Requirements

• Experience designing, implementing, and optimizing multi-step LLM, generative AI, and agentic workflows.
• Strong problem-solving ability to translate complex business challenges into scalable AI solutions.
• Expertise in prompt engineering, human-in-the-loop workflows, and AI performance optimization.
• Ability to troubleshoot and diagnose issues across prompts, agents, context management, integrations, and workflow execution.
• Proficiency in Python, APIs, JSON, Markdown, and working with structured and unstructured data.
• Experience using AI-assisted development tools such as GitHub Copilot, Claude Code, Cursor, or similar platforms.
• Knowledge of agent orchestration frameworks, multi-agent architectures, and modern workflow orchestration patterns.
• Understanding of RAG, context engineering, memory management, tool calling, and agent planning methodologies.
• Experience integrating AI solutions with enterprise applications, automation platforms, cloud AI services, and business processes.
• Strong communication and collaboration with governance, risk, and responsible AI expertise, including evaluation, monitoring, compliance, and operational controls.

Tools & Technologies

• Python, APIs, JSON, Markdown
• GitHub Copilot, Claude Code, Cursor
• RAG, tool calling, multi-agent architectures

Focus Areas

• Agentic AI & Workflow Orchestration: multi-step LLM/agent workflows, tool calling, handoffs, orchestration patterns.
• Prompt & Context Engineering: prompt design, RAG, grounding, context/memory management, human-in-the-loop patterns.
• Hands-On AI Engineering: Python, APIs, JSON, data integration, scripting, AI-assisted development.
• LLM Evaluation & Troubleshooting: testing workflows, diagnosing failures, analyzing outputs/logs, improving accuracy and reliability.
• Business-to-AI Solution Design: translating ambiguous business problems into scalable, structured, end-to-end AI solutions.