AI Projects Where You Can Leverage Development Experience and How to Fill Experience Gaps｜アサインコネクト｜FDE・AI実装の案件

Some engineers who have been working on web application development, business system development, or cloud infrastructure may be wondering, “I want to move into AI projects, but can I get in even with little practical AI experience?”

In this article, we will introduce six AI project requirements received by our company that prioritize development, cloud, and infrastructure experience as essential. We will then analyze whether AI experience was listed as essential or preferred, examine how AI was included in the preferred requirements, and consider how to fill any experience gaps.

This note is written by “Assign Connect AI,” a project introduction service specializing in AI consultants and AI engineers.

We continuously receive AI projects, such as system development incorporating generative AI and support for AI-driven development. In the latter half of this article, we will introduce the requirements for six projects we have actually received.

For those looking for projects where they can leverage their experience in backend, frontend, cloud, or infrastructure development, we provide individual guidance on suitable projects after learning about your past experience and your preferred conditions, such as utilization rate and remote work availability.

Click the link below to open the Assign Connect AI page. You can check project examples, the usage flow, and register your preferred conditions via LINE.

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What you will learn in this article

• What kind of requirements were written for AI projects that prioritized development experience as essential

• The difference between projects where AI experience was essential and those where it was only preferred

• Four forms of AI experience that were included as preferred

• The order in which you should fill your missing AI experience based on your development background

1. Why development experience is required for AI projects

To make generative AI usable in business, simply preparing an AI model is not enough. Tasks identical to traditional system development—such as APIs to connect with internal systems and data, UI screens, cloud environments, and management of permissions and logs—will inevitably arise.

This is also evident from official explanations. For example, in Anthropic’s official documentation, it is explained that Claude’s “tool use (function calling)” returns instructions in a format where Claude calls functions defined by the developer, and it is the application-side code that actually executes those processes. To have AI operate internal systems, development skills are required to create those functions and APIs and ensure they run safely.

Additionally, AWS official documentation describes Amazon Bedrock as a fully managed service that allows enterprises to safely use foundation models from multiple AI companies. Generative AI models are also just one component to be called as a cloud service, so experience in cloud design and construction is directly relevant.

In the six projects introduced later, the core essential requirements were the experience engineers have accumulated, such as backend development, cloud, infrastructure, and CI/CD.

2. Six projects received by our company

From the projects we received between July and October 2026, we will introduce six that prioritize development and infrastructure experience as essential and include generative AI, AI agents, or AI-driven development (development using AI tools) as either essential or preferred. We have rewritten them so that the ordering party cannot be identified. All six projects have also been introduced in other articles on this note.

1. System development utilizing generative AI for an insurance agency (on-site about 2 days a week)

• Required: Experience in backend development with Java/Python, etc., experience in system development utilizing AWS, experience in designing Web APIs/system architectures, basic knowledge of security, and experience in requirements gathering and coordination with clients.

• Preferred: Experience in development utilizing generative AI/LLMs, experience in frontend development with React/TypeScript, etc., experience in full-stack development, and experience in system development in the finance/non-life insurance sector.

2. System development to streamline shareholder meeting operations for a major telecommunications carrier using AI (Infrastructure/Application Development Engineer / AI-driven development / Basically remote)

• Required: Experience in development and operation of frontend and backend for commercial web applications (3+ years each), experience in design, construction, and operation of commercial infrastructure on major cloud services (5+ years) *Experience in either skill is acceptable.

• Preferred: Experience in development and construction using React, Next.js, FastAPI, and Google Cloud, experience in incorporating generative AI into services, and experience in development using AI coding tools (Codex, Claude Code, etc.).

3. Development and implementation of a drone platform (Tech Lead / On-site 1-2 days a week)

• Required: Experience in backend implementation (Go, TypeScript), experience in infrastructure implementation on AWS (ability to visualize functions from architecture diagrams and identify issues, knowledge of non-functional requirements such as security and monitoring), experience with DB (MySQL or PostgreSQL), experience in requirements definition, experience in implementation using AI agents (including scenarios using Claude Code), and experience in team leadership across all phases of SI.

• Preferred: Experience in frontend implementation (any framework is fine), experience in agile development (experience as SM/PO is also welcome), and experience in communicating with section manager to executive-level personnel.

4. Full-stack engineer in the cloud-native domain (Hybrid)

• Required: Practical experience with any of AWS, Azure, or Oracle Cloud, experience as a DevOps engineer, experience in building CI/CD pipelines, ability to follow general technical keywords related to cloud-native, and ability to drive projects as a tech lead.

• Preferred: Experience in AI-driven development.

5. Development of a honeypot monitoring dashboard for a telecommunications carrier research institute (Basically remote)

• Required: Experience in dashboard development with Elasticsearch/Kibana (2+ years or equivalent track record), experience in handling everything from requirements definition to basic design, implementation, and deployment, and the ability to translate business issues and needs into concrete development requirements through coordination and consensus-building with stakeholders.

• Preferred: Experience in development using generative AI tools (Claude Code, Codex, etc.).

6. Support for AI-driven development at a major trust bank (Evangelist/Lead Coach / AWS Q / On-site 5 days a week)

• Required: Experience in organizing overall challenges for AI-driven development and proposing their adoption, experience in utilizing AWS Q (experience in utilizing equivalent AI development tools is also acceptable), and development experience.

• Preferred: Experience in formulating development processes utilizing AI and verifying their application, experience in coaching across cross-functional organizations, and experience in various standardization and knowledge management efforts.

3. What can be learned from these 6 cases

4 cases where AI is only preferred: Requirements are development, cloud, and infrastructure

For the 1st, 2nd, 4th, and 5th projects, AI experience was only listed as a plus. The requirements were for development experience not limited to AI, such as backend development, construction and operation on AWS or major clouds, CI/CD, and dashboard development using Elasticsearch and Kibana.

Just because it is an AI project does not mean you cannot join without practical AI experience; there were projects where development experience itself served as the entry point.

Projects where AI is mandatory: 2 projects: Experience in development using AI tools

The 3rd project required ‘implementation experience using AI agents (including cases using Claude Code),’ and the 6th project required ‘experience using AWS Q (or equivalent AI development tools)’ and experience in proposing task organization and adoption of AI-driven development.

Both are not about the experience of building AI models themselves, but rather the experience of advancing development using AI development tools. The 6th project is a support role for spreading this within an organization, and support for the adoption of AI-driven development is organized in a separate article ‘Projects for Supporting the Adoption of AI-Driven Development: Work to Spread Development Tools like Claude Code and Q within Organizations’.

The AI experience listed as a plus came in four forms

The AI experience listed as a plus was divided into the following four forms.

• Development utilizing Generative AI/LLM (1st project)

• Experience incorporating Generative AI into services (2nd project)

• Development using AI coding tools (Codex, Claude Code, etc.) (2nd and 5th projects)

• AI-driven development (4th project)

The first two are experiences in ‘creating features that use Generative AI,’ and the latter two are experiences in ‘developing using AI.’ It is easier to organize the AI experience seen in AI projects by dividing it into these two directions.

Experience in requirements definition and coordination was also included as a requirement

The 1st project required ‘experience in requirements organization and coordination with clients,’ the 3rd required ‘experience in requirements definition,’ and the 5th required ‘the ability to translate business issues and needs into concrete development requirements through coordination and consensus-building with stakeholders.’ Experience in being involved in the setting of requirements, not just implementation, is also being looked at.

Since there are only 6 projects, this cannot be said to be a trend for the entire market.

For those who are curious about which project your development experience is closest to: If you register your desired conditions via LINE, we will individually guide you when a similar AI project becomes available.

See how to receive AI project information

4. How to fill experience gaps

Following the reading of Chapter 3, when moving from an engineer to an AI role, it is thought that adding experience in the following order will expand the range of projects you can join. This is one perspective organized from the terminology used in project requirements.

1. Make it possible to explain your current development experience using the terminology of AI projects

For 4 of the projects, AI experience was only preferred, while development, cloud, and infrastructure experience were mandatory. First, be prepared to explain your experience in terms of the languages used, cloud platforms, API design, and non-functional requirements like security and monitoring, including the scale and extent of your responsibilities.

2. Use AI development tools in your daily work

The preferred requirements for the 2nd and 5th projects, and the mandatory requirements for the 3rd and 6th, included development using AI tools. Official documentation describes Claude Code (Anthropic) as an agentic coding tool that reads codebases, edits files, executes commands, and integrates with development tools; it can be used in terminals, IDEs, desktop apps, and browsers. The Codex (OpenAI) CLI is also described as being able to examine code, make changes, and execute commands from the terminal. Being able to explain which tasks you delegated to AI and which parts you verified yourself counts as experience that meets these criteria.

3. Incorporate generative AI into systems as small features

The preferred requirements for the 1st and 2nd projects involved development using generative AI or integration into services. As with the tool usage in Chapter 1, if you try building small features yourself that call generative AI APIs and connect them to internal data or systems, you will be able to explain in your own words how to handle prompts, how to deal with the fact that AI answers are not always the same, and how to manage costs.

4. Get involved in the requirements-setting process

For the 1st, 3rd, and 5th projects, experience in requirements definition and coordination with stakeholders was mandatory. In jobs that involve introducing AI into business operations, there are many situations where you must decide with business stakeholders what tasks to delegate to AI. Even in your current development job, if you gain experience by joining meetings to determine requirements and translating those decisions into specifications, that experience will be directly applicable to AI projects.

How to write about your experience is organized in another article, “Resumes That Get Evaluated for AI Projects: How Engineers and Consultants Should Write Them”. Conversely, for the path to moving from business experience to AI work, please see another article, “Transitioning from Consultant to AI Roles: AI Projects That Leverage Business Experience and How to Bridge Skill Gaps”.

The role of talking to clients to determine requirements and implementing AI until it is operational in business is sometimes called a Forward Deployed Engineer (FDE). For how this position relates to those with development experience, please see another article, “What is an FDE (Forward Deployed Engineer)? The Job of Implementing AI in Business and Required Experience”.

5. Frequently Asked Questions

Can I join an AI project even if I don’t have practical AI experience?

Out of the 6 projects this time, 4 only listed AI experience as preferred, while development, cloud, and infrastructure experience were mandatory. There are projects you can join, but as with the remaining 2, there are also projects that make experience with AI development tools mandatory. Since conditions vary by project, please check each one individually.

Is experience in building machine learning models necessary?

The conditions for the 6 projects this time did not include experience in building machine learning models. The AI experience mentioned was development using generative AI, integration into services, and development using AI development tools. However, these 6 projects were selected only for this theme and do not represent all AI projects.

Can infrastructure/cloud engineers also be candidates for AI projects?

For the 2nd project, 5+ years of experience in the design, construction, and operation of commercial infrastructure on major clouds was mandatory, and it was stated that “either skill is acceptable” alongside development experience. The 4th project also required cloud and CI/CD experience, with AI-driven development being preferred.

Summary

• Of the 6 AI projects we received that focused on mandatory development and infrastructure experience, 4 only listed AI as preferred, while development, cloud, and infrastructure experience were mandatory

• The 2 projects where AI was mandatory both required experience in development using AI development tools

• The preferred AI experience came in 4 forms: “development using generative AI,” “integration of generative AI into services,” “development using AI coding tools,” and “AI-driven development”

• To bridge experience gaps, it is effective to follow this sequence: explain your current development experience using the terminology of AI projects, use AI development tools, integrate generative AI as a small feature, and join the process of defining requirements. This approach makes it easier to gain experience that aligns with the requirements.

For the role of implementing AI into business operations, please also see “What is an FDE (Forward Deployed Engineer)? | The Job of Implementing AI into Business and Required Experience”, and for the differences between consultant and engineer roles, please see “Difference Between AI Consultants and AI Engineers | Why People Who Can Bridge Requirement Definition to Implementation Are in Demand”.

Assign Connect AI is a project introduction service specializing in AI consultants and AI engineers.

By registering your preferred conditions via LINE, you can receive personalized AI project opportunities.

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