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 intellectual property

These materials must not be:

disclosed
published
redistributed
sold
shared with third parties
included in portfolios
included in case studies
reused for other clients
uploaded to unauthorised services

except where expressly authorised in writing.

The final signed NDA and contractor agreement will govern confidentiality, intellectual property, permitted use, data handling, subcontracting and post-engagement obligations.

Intellectual Property

Applicants must disclose before engagement any pre-existing frameworks, third-party materials, licensing restrictions, subcontractors or other elements they propose to incorporate that could affect ownership or commercial use.

The final contractor agreement will govern ownership and rights in commissioned deliverables.

Fixed-Price Engagement

This is intended to be a fixed-price engagement for the agreed scope.

Your bid should reflect the full amount you genuinely require to complete the work to the required standard.

Before bidding, assess:

audit effort
research required
testing required
complexity
likely revisions
software/tools required
third-party costs
specialist assistance
time required

Do not submit an artificially low bid with the expectation of automatically renegotiating the price after award.

Any genuine material expansion of scope must be agreed in writing before additional work begins.

Quality Standard

The required standard is premium, production-grade and best-in-class.

The final system should be:

rigorous
reliable
scalable
evidence-aware
multimodal
context-efficient
practically executable
resilient to failure
maintainable
simple for the end user

Completion is not defined by prompt length or time spent.

The final deliverables must materially satisfy the agreed specification.

Who Should Apply

We are particularly interested in people who think like:

AI systems architects
agent engineers
context engineers
multimodal workflow designers
generative-media technologists
AI evaluation specialists

Experience with professional film, documentary, VFX, editing, animation or research workflows is an advantage.

Who Should Not Apply

Please do not apply if your experience is primarily:

basic ChatGPT prompting
prompt packs
generic AI copywriting
simple role prompts
making prompts longer without improving architecture

This project requires advanced systems-level expertise.

Application Questions

Please answer:

What is the most sophisticated AI agent or system-prompt architecture you have worked on?
How do you approach long-context and long-running AI workflows?
How do you test whether a revised prompt or agent system is genuinely better?
How do you design systems that remain reliable when models or tools fail?
What experience do you have with generative image/video systems?
What experience do you have with rigorous factual or historical research?
What experience do you have with Google Flow/Veo or comparable platforms?
What is your proposed approach to auditing an existing sophisticated system without simply replacing it with a generic rewrite?

Please keep your answers technically specific.

Generic AI-generated applications may not be considered.

Important

The public listing intentionally describes the project at a high level.

Detailed architecture and proprietary requirements will only be provided after appointment, NDA execution and contractor agreement.