Background
I’m currently working as an AI Software Engineering Intern, with an MSc in Applied AI and Data Science (Distinction) and a BSc in Software Engineering. I’ve worked on multiple impactful projects in AI, IoT, and open source, with some gaining public traction and real-world usage.
My LORs
• LOR from my MSc supervisor.
• LOR from my line manager (internship).
• LOR from a senior tech professional in the industry.
Mandatory Criteria
• Recognition from a senior figure at Annalise.ai for my AI-powered chest X-ray analysis and report generation tool.
• Strong public traction from open-source projects (e.g., football prediction app with over ₦1.9 million won by a user, 20k+ impressions on X/Twitter).
OC1 – Innovation as an Employee
• Developed a real-time AI-based football prediction system (used publicly with proven results).
• Built an IoT Inventory Management System for traffic device monitoring (Kafka, CouchDB, Fission Functions).
• Currently working on FitGenie Pro – an AI fitness coach with Web3 integration (Encode Hackathon project).
OC2 – Recognition Outside Immediate Work
• Active GitHub projects with collaborators and pull requests.
• Positive feedback and adoption of projects shared publicly on Twitter.
• Email recognition from external professionals outside my employment.
OC3 – Significant Technical Contribution
• YuDetect: Led MQTT integration and message formatting for real-time vehicle classification.
• Authored technical documentation and helped prepare system for customer field trials.
• Open-source work on multiple AI tools and systems.
Career Stage
• MSc with Distinction and £10K scholarship.
• 2 years of project-based and internship experience in AI/ML engineering.
• Strong contribution to product-led systems in traffic, health, and fitness domains.
Your combination of academic achievements and hands-on AI projects shows good potential, but you can sharpen the focus for visa requirements. Including some testimonials for your open-source tools would show broader impact beyond your immediate circle.
The technical projects need clearer proof of real-world use. For example: How many hospitals tested the chest X-ray tool? What error rate reduction did YuDetect achieve in field trials? Screenshots of user testimonials from your Twitter followers who won bets using your prediction app could strengthen OC2 evidence.
Three quick fixes: 1) Add a salary comparison showing you earn above UK median for AI roles. Also add other contributions on the evidence apart from salary, 2) Get a recommendation from an external developer using your open-source code, 3) Include traffic/usage stats from your GitHub projects. Mission.dev is not considered a product company under visa rules. I’ve seen an application get rejected - which included that company.
Thank you again for your feedback so far – it’s been really valuable.
I’d love some clarification on a few points based on your suggestions and my current evidence:
OC2 – Recognition Outside Immediate Work
I’ve shared projects like my AI football prediction system publicly, and I plan to include screenshots of people on Twitter who won money using the app (including one who won over ₦15 million).
Would this kind of social proof (user feedback and winnings) be strong enough for OC2?
Also, if the code for a major project like YuDetect is private (hosted on GitLab due to company policy), can I use screenshots, commit logs, architecture diagrams, and public mentions (like tweets or internal emails) as proof?
MC – Real-world Impact
For my AI-powered chest X-ray tool, I have an endorsement email from a senior figure at Annalise.ai.
For my IoT project (YuDetect), we’re preparing for a field trial in Manchester soon.
Would combining this with internal screenshots, logs, and diagrams show enough real-world usage even if it’s not publicly available yet?
Salary Evidence for MC
I currently earn £24,000/year as an AI Engineering Intern. Glassdoor puts the average UK intern salary around £23,427/year.
Would this be strong enough for Exceptional Promise under the salary benchmark?
LORs
I have 3 letters: from my line manager, an academic, and a senior industry professional.
Would adding a short endorsement or email from someone outside my company (e.g. GitHub user or Twitter follower) help further?
1.1. That should be fine, try to gather evidences of open-source contributions as well
1.2. Private projects done as a part of a company are more relevant for OC3
2. In that case, try to get a reference letter as well.
3. Since this is as a part of an internship, that wouldn’t be relevant evidence.
4. Line manager is no longer relevant, has to be someone who isn’t directly involved in your work.