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About This Role
The Opportunity
We are offering a 6\-month Industry Placement for highly motivated students looking to gain hands\-on experience in Artificial Intelligence, Automation, and modern software systems.
This is not a typical internship.
You will work on multiple real\-world projects, including internal systems, client work, and a confidential stealth AI product (under NDA). The role is designed to give you production\-level exposure, helping you build a strong, job\-ready portfolio.
You will gain experience with modern AI tools, LLM\-based applications, automation workflows, and backend systems used in real businesses today.
What You’ll Be Working On
- Building AI agents, chatbots, and LLM\-powered applications
- Designing automation workflows (n8n, APIs, event\-driven systems)
- Developing backend logic using Python and modern API frameworks
- Working with OpenAI, Claude, and other LLM ecosystems
- Creating data pipelines, analytics workflows, and AI integrations
- Contributing to a stealth AI project (under NDA)
- Collaborating in an Agile environment (stand\-ups, sprint planning, reviews)
- Delivering real, production\-ready systems
Tech Stack \& Tools
- Python (Core \+ AI integrations)
- LLM APIs (OpenAI, Claude, etc.)
- Automation tools (n8n, Zapier, Make)
- REST APIs \& backend systems
- Git, GitHub, version control
- AI workflows (Agents, RAG, prompt engineering)
Full access to latest AI tools and platforms will be provided by the company
Who This Is For
Students currently pursuing a degree in:
- Computer Science
- Data Science
- Artificial Intelligence
- Software Engineering
- Mathematics or related fields
Requirements
- Basic to intermediate Python programming skills
- Understanding of programming fundamentals and logic building
- Familiarity with Git / GitHub
- Strong interest in:
- AI / Machine Learning
- Automation \& AI agents
- Real\-world product development
- Ability to work independently in a remote setup
- Good communication and documentation skills
Bonus Skills (Preferred but not required)
- Experience with APIs or backend systems
- Knowledge of LLMs, prompt engineering, or RAG systems
- Familiarity with automation tools (n8n, Zapier, Make)
- Exposure to Machine Learning frameworks (TensorFlow, PyTorch, Scikit\-learn)
- Basic understanding of full\-stack development
What You Will Gain
- Hands\-on experience with production\-level AI systems
- Work on multiple real\-world projects \+ stealth AI product
- Build a strong GitHub portfolio with real deliverables
- Exposure to modern AI architecture and automation systems
- Mentorship from experienced engineers
- Clear understanding of how AI systems are deployed in businesses
Important Terms
- Unpaid placement (training\-focused opportunity)
- Duration: 6 months (mandatory commitment)
- Working Hours: 35 hours/week (tracked via internal CRM)
- Start Date: 1st May 2026
- Contract: NDA required (due to client and stealth project work)
Certificate of Achievement will only be issued upon successful completion of the placement and full compliance with contractual hours and deliverables
High\-performing candidates may be considered for a full\-time paid role
Application Process (Mandatory)
To be considered for this role, you must complete the following:
1\. Cover Letter
- Explain your interest in AI and automation
- Highlight relevant projects, skills, or experience
- Keep it concise and practical (no generic content)
2\. Loom Video Submission (2–5 minutes)
Please record a short Loom video covering:
- A brief introduction about yourself
- Walkthrough of a project you have built (AI, automation, or development\-related)
- Your approach to solving problems or learning new technologies
Applications without a Loom video will not be considered
3\. Portfolio / GitHub (If Available)
- Share links to your projects, GitHub, or demos
Selection Philosophy
We are looking for builders, not just applicants.
Your ability to demonstrate what you have built and how you think matters more than academic scores.
About Puzzle Metrics
Puzzle Metrics is an AI\-focused company building agentic AI systems, automation platforms, and scalable software solutions.
We work at the intersection of AI engineering, automation, and product development, helping businesses implement practical, high\-impact AI systems.
Why This Role Stands Out
- Work on real projects, not dummy tasks
- Exposure to latest AI tools and systems
- Opportunity to contribute to a stealth AI product
- Build a portfolio that actually gets you hired
- Direct experience in AI engineering and automation workflows
Job Types: Contract, Internship
Work Location: Remote
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Puzzle Metrics Limited, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Puzzle Metrics Limited AI Hiring
Puzzle Metrics Limited has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (2,416) are outnumbered by mid-level (16,247) and senior (5,153) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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