AI Engineer for Generative Media System Improvement

Senior AI Agent / Prompt Architect for Advanced Generative Media System We are seeking a highly experienced AI Agent Architect / Prompt Engineer / Context Engineer to audit, refine and productionize an advanced proprietary generative-media system. This is not a basic prompt-writing role. A substantial system already exists. The successful contractor will be responsible for independently reviewing it, identifying weaknesses, researching relevant current best practices, testing it, and improving it into a robust production-ready solution. Detailed system architecture, prompt logic, workflows and proprietary methodology will not be disclosed publicly and will only be shared with the selected contractor after the required confidentiality and contractor agreements are signed. Project Objective The system is intended to help an AI agent turn simple user input—such as a topic, script, narration, screenplay, treatment or production brief—into a high-quality generative-media production workflow. The system must be designed for: strong autonomous decision-making high-quality multimodal output factual and historical accuracy where required reliable operation over complex projects consistency across generated media scalable context management robust quality control effective error recovery efficient use of available AI models and tools Projects may range from short content to substantially longer-form productions. There is no fixed five-minute architectural limit. Historical / Factual Accuracy For factual, documentary and historical projects, maximum demonstrable accuracy is a core requirement. The architecture should support rigorous research and verification rather than relying on general model knowledge or superficial source checking. It should be capable of: using authoritative and primary sources where appropriate cross-checking important claims identifying conflicting evidence distinguishing fact from interpretation distinguishing documented evidence from reconstruction identifying uncertainty avoiding unsupported historical invention maintaining appropriate source provenance supporting later fact-checking and editorial review The governing principle should be: The system should achieve the highest factual and historical accuracy reasonably supportable by available evidence and must never present unsupported reconstruction as established fact. The research process itself should be capable of being audited. Scope of Expertise Required We are interested in candidates with strong experience in several of the following: advanced prompt engineering AI agent architecture context engineering multimodal AI systems workflow orchestration long-running agent workflows structured state and persistence AI evaluation and quality assurance generative image systems generative video systems Google Flow / Veo Runway, Kling, Sora or comparable systems automated testing failure recovery factual research and verification documentary or historical research filmmaking / VFX / production workflows The role requires systems-level thinking rather than simply the ability to write long prompts. Key Requirements The successful contractor should be able to: audit a large existing AI system prompt identify contradictions, omissions and weak assumptions distinguish useful complexity from unnecessary complexity improve reliability and execution improve long-form scalability improve context efficiency improve multimodal workflow design improve consistency across generated outputs improve research and factual-verification methodology improve quality-control methodology design sensible recovery behaviour when tools or generations fail prevent the system from assuming capabilities that are not actually available ensure the solution remains maintainable as AI models change Testing Requirement This project must include meaningful testing. We do not want a contractor who simply rewrites the prompt and declares it finished. The revised system should be tested against a range of representative scenarios, including: short and longer-form projects factual and historical work supplied scripts projects requiring strong visual consistency difficult multimodal generation tasks interrupted or resumed workflows unavailable tool capabilities repeated generation failures The contractor should use observed behaviour to guide revisions. Evaluation We are interested in measurable improvement. The contractor should propose a practical evaluation methodology covering areas such as: instruction adherence factual integrity research quality consistency reliability context efficiency multimodal quality failure recovery scalability overall production quality Deliverables Expected deliverables include: Independent audit of the existing system Gap analysis Review of relevant current technologies and best practices Recommendations for architectural improvements Revised production-ready master system prompt Improved context and workflow strategy Improved factual-research and verification methodology Improved QC and failure-recovery methodology Long-form scalability recommendations Testing methodology Stress-test findings Final revised deployment-ready system Concise technical documentation explaining major changes The contractor should not make the system longer merely for the sake of complexity. The goal is to make it better, more reliable and more operational. Confidentiality — Mandatory This engagement involves confidential and proprietary intellectual property. The successful contractor must sign our NDA/confidentiality agreement and contractor agreement before any work begins and before any confidential system materials are supplied. This is a mandatory condition of the engagement. Do not bid if you are unwilling to sign the required agreements. Detailed materials may include: proprietary prompts system instructions production methodology workflow designs internal research evaluation methods test materials implementation logic other confidential intelle