Agentic AI Engineer - Workflow Orchestration & Prompt Engineer

Agentic AI & Workflow Orchestration:

Designing multi-step LLM/agent workflows, tool calling, handoffs, and orchestration patterns.

Prompt & Context Engineering:
Strong prompt design, RAG, grounding, context/memory management, and human-in-the-loop patterns.

Hands-on AI Engineering:
Strong Python, APIs, JSON, data integration, scripting, and AI-assisted development.

LLM Evaluation & Troubleshooting:
Testing workflows, diagnosing failures, analyzing outputs/logs, and improving accuracy and reliability.

Business-to-AI Solution Design:
Translating ambiguous business problems into scalable, structured, end-to-end AI solutions.

JOB DESCRIPTION

+ AI Workflow Designer & Prompt Architect to design, develop, and optimize AI-powered workflows using large language models (LLMs), AI agents, and orchestration frameworks. The role focuses on hands-on workflow architecture, prompt engineering, technical implementation, experimentation, and operational reliability. This individual translates complex or ambiguous requirements into structured, multi-step workflows that coordinate prompts, tools, context, data, and agent interactions. The role requires strong analytical and engineering skills to independently assess system behavior, prototype solutions, troubleshoot execution issues, and improve AI performance through testing and operational insights.

+ In this role, you will:

+ Translate business problems into structured AI workflow designs that break complex tasks into logical stages and execution paths.

+ Design and maintain reusable prompt libraries, workflow templates, and orchestration patterns, with standardized templates, version control, evaluation criteria, change tracking, and lifecycle management.

+ Design, develop, and optimize multi-step prompt workflows that enable AI agents to perform reasoning, decision support, content generation, analysis, and task execution activities.

+ Define workflow architecture including prompts, context requirements, tool interactions, orchestration logic, handoffs, dependencies, and output requirements.

+ Determine how information, memory, context, and intermediate outputs should be managed throughout workflow execution.

+ Analyze workflow execution logs, model outputs, and intermediate agent responses to identify root causes of failures, performance degradation, or inconsistent results.

+ Troubleshoot issues related to prompt design, context management, agent behavior, tool integrations, data pipelines, file structures, APIs, and execution environments.

+ Evaluate workflow effectiveness using qualitative and quantitative measures and implement improvements to increase reliability, accuracy, efficiency, and user outcomes.

+ Improve context engineering strategies to optimize information 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 activities to support AI 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 informed on emerging AI technologies, agent frameworks, orchestration platforms, prompt engineering techniques, and industry best practices.

Qualifications:

+ Experience designing, implementing, and optimizing multi-step LLM, generative AI, and agentic workflows.

+ Strong problem-solving skills with the ability to translate complex business challenges into scalable AI solutions.

+ Expertise in prompt engineering, human-in-the-loop workflows, and AI performance optimization.

+…