/install ai-ml-engineer
Overview
The AI/ML Engineer Roadmap API is a professional career development platform designed to help aspiring and current engineers navigate the complex path to entry-level AI/ML engineering roles. By analyzing your current experience, existing technical skills, and career aspirations, this API generates a customized learning roadmap that bridges gaps between where you are today and where you want to be.
This platform is ideal for self-taught developers transitioning into machine learning, computer science graduates seeking specialization, and career-changers aiming to enter the AI/ML industry. The API leverages assessment data to create personalized guidance, ensuring that your learning path is efficient, relevant, and aligned with real-world industry requirements.
Key capabilities include comprehensive skill gap analysis, personalized curriculum recommendations, milestone tracking through session management, and continuous roadmap refinement based on your evolving profile. Whether you're starting from fundamentals or building on existing knowledge, this roadmap generator ensures a structured approach to career advancement in AI/ML engineering.
Usage
Sample Request
{
"assessmentData": {
"experience": {
"yearsInIT": 2,
"previousRoles": ["Software Developer", "Data Analyst"],
"industryBackground": "Finance"
},
"skills": {
"programming": ["Python", "SQL", "Java"],
"mathematics": ["Statistics", "Linear Algebra"],
"ml_frameworks": ["Scikit-learn"]
},
"goals": {
"targetRole": "ML Engineer",
"timeline": "12 months",
"specialization": "Computer Vision"
},
"sessionId": "sess_abc123def456",
"timestamp": "2024-01-15T10:30:00Z"
},
"sessionId": "sess_abc123def456",
"userId": 42,
"timestamp": "2024-01-15T10:30:00Z"
}
Sample Response
{
"roadmapId": "roadmap_xyz789",
"userId": 42,
"sessionId": "sess_abc123def456",
"generatedAt": "2024-01-15T10:30:15Z",
"timeline": "12 months",
"phases": [
{
"phase": 1,
"title": "Foundation Strengthening",
"duration": "3 months",
"focus": ["Advanced Python", "Mathematics for ML", "Data Structures"],
"resources": ["Andrew Ng's ML Specialization", "Linear Algebra by 3Blue1Brown"],
"milestones": ["Complete Python fundamentals", "Master linear algebra basics"]
},
{
"phase": 2,
"title": "Core ML Concepts",
"duration": "3 months",
"focus": ["Supervised Learning", "Unsupervised Learning", "Model Evaluation"],
"resources": ["Hands-On Machine Learning book", "Kaggle competitions"],
"milestones": ["Build 3 end-to-end projects", "Achieve 80% accuracy on benchmark"]
},
{
"phase": 3,
"title": "Computer Vision Specialization",
"duration": "4 months",
"focus": ["CNN architectures", "Image preprocessing", "Transfer Learning", "Object Detection"],
"resources": ["Fast.ai Computer Vision course", "OpenCV documentation"],
"milestones": ["Complete 2 CV projects", "Understand ResNet and VGG"]
},
{
"phase": 4,
"title": "Industry Readiness",
"duration": "2 months",
"focus": ["Production ML", "Model deployment", "Portfolio building", "Interview prep"],
"resources": ["MLOps.community resources", "System design for ML"],
"milestones": ["Deploy model to cloud", "Complete portfolio with 5+ projects"]
}
],
"skillGaps": [
{
"skill": "Deep Learning Frameworks",
"current": "Beginner",
"required": "Advanced",
"priority": "High"
},
{
"skill": "Production ML Engineering",
"current": "None",
"required": "Intermediate",
"priority": "High"
},
{
"skill": "Cloud Platforms (AWS/GCP)",
"current": "Beginner",
"required": "Intermediate",
"priority": "Medium"
}
],
"recommendations": [
"Focus on TensorFlow and PyTorch for deep learning",
"Build projects with real-world datasets from Kaggle",
"Contribute to open-source ML projects to gain practical experience",
"Practice system design for ML systems",
"Network with ML engineers on LinkedIn and in local communities"
]
}
Endpoints
GET /
Description: Root endpoint that returns basic API information.
Parameters: None
Response: Basic API metadata object.
GET /health
Description: Health check endpoint to verify API availability and operational status.
Parameters: None
Response: Health status object indicating the API is operational.
POST /api/aiml/roadmap
Description: Generate a personalized AI/ML engineering career roadmap based on assessment data, experience level, current skills, and career goals.
Request Body Parameters:
| Name | Type | Required | Description |
|---|---|---|---|
| assessmentData | AssessmentData object | Yes | Contains experience, skills, goals, sessionId, and timestamp. The experience field is an object capturing years in IT, previous roles, and industry background. The skills field is an object documenting programming languages, mathematics knowledge, and ML frameworks. The goals field is an object specifying target role, timeline, and specialization area. |
| sessionId | String | Yes | Unique identifier for this assessment session, used for tracking and correlating requests. |
| userId | Integer or Null | No | Optional user identifier for authenticated requests; omit or set to null for anonymous assessments. |
| timestamp | String | Yes | ISO 8601 formatted timestamp indicating when the roadmap request was initiated. |
Response Schema:
{
"roadmapId": "string",
"userId": "integer or null",
"sessionId": "string",
"generatedAt": "string (ISO 8601 timestamp)",
"timeline": "string",
"phases": [
{
"phase": "integer",
"title": "string",
"duration": "string",
"focus": ["string"],
"resources": ["string"],
"milestones": ["string"]
}
],
"skillGaps": [
{
"skill": "string",
"current": "string (proficiency level)",
"required": "string (proficiency level)",
"priority": "string (High/Medium/Low)"
}
],
"recommendations": ["string"]
}
Error Response (422 Validation Error):
If required fields are missing or validation fails, the API returns a 422 Validation Error with details on the specific fields that failed validation.
Pricing
| Plan | Calls/Day | Calls/Month | Price |
|---|---|---|---|
| Free | 5 | 50 | Free |
| Developer | 20 | 500 | $39/mo |
| Professional | 200 | 5,000 | $99/mo |
| Enterprise | 100,000 | 1,000,000 | $299/mo |
About
ToolWeb.in - 200+ security APIs, CISSP & CISM, platforms: Pay-per-run, API Gateway, MCP Server, OpenClaw, RapidAPI, YouTube.
- toolweb.in
- portal.toolweb.in
- hub.toolweb.in
- toolweb.in/openclaw/
- rapidapi.com/user/mkrishna477
- youtube.com/@toolweb-009
References
- Kong Route: https://api.mkkpro.com/career/ai-ml-engineer
- API Docs: https://api.mkkpro.com:8055/docs
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install ai-ml-engineer - 安装完成后,直接呼叫该 Skill 的名称或使用
/ai-ml-engineer触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
AI ML Engineer 是什么?
Generate personalized AI/ML engineering career roadmaps based on individual experience, skills, and goals. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 146 次。
如何安装 AI ML Engineer?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install ai-ml-engineer」即可一键安装,无需额外配置。
AI ML Engineer 是免费的吗?
是的,AI ML Engineer 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
AI ML Engineer 支持哪些平台?
AI ML Engineer 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。
谁开发了 AI ML Engineer?
由 ToolWeb(@krishnakumarmahadevan-cmd)开发并维护,当前版本 v1.0.0。