Description review
AI Tools & Automation Intern (Developer)
Hudson Manpower · Remote · back to the listing
HR standards
54/100
needs work
Title ↔ description
70/100
solid
Reads as
Machine Learning Engineer
80% confident
What this role officially is
data scientist — ESCO, the EU occupation classification
Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.
Also known as: data scientists, data engineer, research data scientist, data expert, data research scientist
How others title the same work
Large employers
- Applied AI Engineer Automattic
- Machine Learning Engineer, CX Intelligence Coinbase
- AI Engineer - FDE (Forward Deployed Engineer) Databricks
- AI Engineer - FDE (Forward Deployed Engineer) - U.S. Federal Sector Databricks
- Senior AI Engineer – Notebooks Datadog
Startups
- Attendi | Medior Machine Learning Engineer | Amsterdam, Netherlands | ONSITE (hybrid) | €6,000 - €7,000 per month | Full-time (80–100%, ~4–5 days/week) | Visa sponsorship + 30% ruling possible Attendi
- Member of Technical Staff (applied) Anthrogen
- Aptura AI | Full-Time | MTS (Applied AI), MTS (SWE / Product) | London | ONSITE / HYBRID Aptura AI
- Arcforma AI (arcforma.ai) | AI Engineer (Marketing / Construction / Arcforma AI (arcforma.ai)
- Staff AI Engineer - Agent Architecture & Behavior Artisan
What the listing never says
- 25 bullet points. Long requirement lists deter qualified candidates, who read them as hard gates. Scope clarity
- No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency
The listing, marked up
Nothing in the wording of this listing tripped a check. The scores above still judge how complete and coherent it is.
Job Role: AI Developer Intern (LLM + MCP + AI Trends)
Role Overview:
We are hiring an AI Developer Intern who can both:
• Build AI systems (LLMs, MCP servers, APIs)
• Continuously track and evaluate new AI tools & releases
This is a builder + researcher hybrid role, but execution > research.
Key Responsibilities:
•
AI Development (Primary Focus)
• Build applications using: OpenAI, Anthropic, Google DeepMind
• Implement: Tool/function calling, Context handling, Prompt pipelines
•
MCP Server & AI Systems
• Build and maintain MCP (Model Context Protocol) servers
• Create tools that LLMs can use: APIs, Internal systems
• Design: Multi-step workflows, Structured outputs
•
AI Tools & Trends Tracking (Important)
• Stay updated with: New AI tools launches, Model updates, Dev frameworks
• Sources to track: Twitter (AI builders), Product Hunt, GitHub trending
• Filter: What is useful vs hype
•
Rapid Prototyping
• Build quick POCs using new tools
• Example: Try new model → integrate → test → report
• Convert useful tools into: Internal features, Product improvements
•
Weekly Intelligence Reports
• Share: 5–10 new tools, 2 tools worth implementing, 1 working demo/POC
Requirements
Required Skills:
• Must-Have: Python or JavaScript (strong basics), API understanding, Basic LLM knowledge: Tokens, Context Prompting, Critical (Filter Here), Can build, not just explore, Understands: MCP / tool calling, How LLM apps actually work, Cursor, Antigravity
• Good to Have: RAG / vector DB, FastAPI / Node backend, GitHub projects
Ideal Candidate:
• Builds side projects
• Actively explores new AI tools
• Thinks: “How can I use this in real product?”
• Not a YouTube learner, a doer
Highlights
Build LLM apps & MCP servers, test latest AI tools, ship real features, work closely with founders, fast growth, real product impact
Originally posted on Himalayas
Role Overview:
We are hiring an AI Developer Intern who can both:
• Build AI systems (LLMs, MCP servers, APIs)
• Continuously track and evaluate new AI tools & releases
This is a builder + researcher hybrid role, but execution > research.
Key Responsibilities:
•
AI Development (Primary Focus)
• Build applications using: OpenAI, Anthropic, Google DeepMind
• Implement: Tool/function calling, Context handling, Prompt pipelines
•
MCP Server & AI Systems
• Build and maintain MCP (Model Context Protocol) servers
• Create tools that LLMs can use: APIs, Internal systems
• Design: Multi-step workflows, Structured outputs
•
AI Tools & Trends Tracking (Important)
• Stay updated with: New AI tools launches, Model updates, Dev frameworks
• Sources to track: Twitter (AI builders), Product Hunt, GitHub trending
• Filter: What is useful vs hype
•
Rapid Prototyping
• Build quick POCs using new tools
• Example: Try new model → integrate → test → report
• Convert useful tools into: Internal features, Product improvements
•
Weekly Intelligence Reports
• Share: 5–10 new tools, 2 tools worth implementing, 1 working demo/POC
Requirements
Required Skills:
• Must-Have: Python or JavaScript (strong basics), API understanding, Basic LLM knowledge: Tokens, Context Prompting, Critical (Filter Here), Can build, not just explore, Understands: MCP / tool calling, How LLM apps actually work, Cursor, Antigravity
• Good to Have: RAG / vector DB, FastAPI / Node backend, GitHub projects
Ideal Candidate:
• Builds side projects
• Actively explores new AI tools
• Thinks: “How can I use this in real product?”
• Not a YouTube learner, a doer
Highlights
Build LLM apps & MCP servers, test latest AI tools, ship real features, work closely with founders, fast growth, real product impact
Originally posted on Himalayas