Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Build and improve AI systems for fraud detection in documents and images.
- Company: Veridox, a pioneering tech company focused on innovative fraud detection solutions.
- Benefits: Competitive salary, equity options, and remote work flexibility.
- Why this job: Join a small team to create impactful AI solutions that make a real difference.
- Qualifications: Strong Python skills, experience with LLMs, and AWS familiarity.
- Other info: High autonomy, async-first culture, and excellent growth opportunities.
The predicted salary is between 36000 - 60000 £ per year.
Location: Remote (UK-based)
Type: Full-time, permanent
Compensation: Competitive salary + equity available
About Veridox
Veridox detects fraud in documents and images submitted to insurance companies, airlines, and financial institutions. When someone submits a receipt, ID document, or damage photograph, our platform determines whether it's genuine, tampered with, or AI-generated and explains why. Under the hood, we’re replacing brittle, hand‑wired pipelines with multi‑agent AI that reasons more like a human forensic analyst: pulling signals from multiple tools, weighing messy, conflicting clues, and landing on clear, defensible decisions.
The Role
You’ll work on the core AI systems that make fraud decisions. This means building and improving the agent pipelines that coordinate LLMs, VLMs, OCR and forensic tools. You won’t be just calling APIs, but designing how autonomous agents reason about complex, ambiguous evidence.
Day to day, you might be:
- Improving how the system handles a document type it's getting wrong
- Designing a new agent reasoning pattern (e.g., cross-referencing findings across multiple documents in a claim)
- Debugging why an agent produces inconsistent verdicts on edge cases
- Deploying pipeline changes to AWS and monitoring real-world performance
- Writing tests that verify an agent makes the right routing decision when evidence conflicts
The work sits at the intersection of AI engineering, systems design, and a surprisingly interesting fraud detection domain.
Builder Mindset
We’re looking for people who build systems, not just talk about them. This role is about shipping reliable AI into production: debugging edge cases, improving pipelines, and learning from real-world behaviour. If you enjoy turning ideas into working software and iterating quickly, you’ll fit right in.
What We’re Looking For
You should be strong in:
- Python: you write clean, typed code and care about it working correctly at the edges
- Working with LLMs beyond chat: structured output, multi-step reasoning, prompt calibration for reliability
- AWS: comfortable with S3, Lambda, IAM, and deploying services that need to work without you watching them
- LLM/VLM Observability (e.g Arize Phoenix)
- Testing code that involves non-deterministic systems
Ideally you also bring:
- Experience with agent orchestration frameworks (LangGraph, LangChain, or similar)
- Familiarity with vision-language models for image analysis
- Some exposure to document analysis, OCR, or fraud detection
- Opinions about when agents are useful and when they’re over-engineering
We don’t expect you to know all of:
- The specific AWS services we use (Bedrock AgentCore, EventBridge)
- Our domain (insurance fraud, document forensics)
- The exact frameworks in our stack
These are learnable. What matters more is that you can reason about systems, write reliable code, and think critically about how AI makes decisions.
How We Work
Small team, high autonomy, async-first. Security-conscious by default. We assume all inputs are hostile. We’d rather halt and ask a question than ship something we’re uncertain about. Code gets attacked before it ships. We review from the perspective of a hostile auditor. We don’t over-engineer. Three similar lines of code is better than a premature abstraction.
What Growth Looks Like
Early on: You’re deploying changes to the pipeline, understanding how evidence flows through agents, and shipping features that touch the reasoning layer.
Later: You own major subsystems. The document analysis agent, the tool orchestration layer, or the cross-document case analysis pipeline. You’re making architectural decisions about how agents reason.
Longer term: You’re designing novel multi-agent patterns, integrating new forensic tools, and shaping how the platform learns from analyst feedback over time.
Senior Applied AI Engineer employer: Veridox
Contact Detail:
Veridox Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Applied AI Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the AI and tech space on LinkedIn or at meetups. A friendly chat can lead to opportunities that aren’t even advertised yet.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving AI systems or fraud detection. This gives potential employers a taste of what you can do.
✨Tip Number 3
Prepare for interviews by brushing up on your problem-solving skills. Be ready to discuss how you’d tackle real-world challenges in AI engineering, especially around document analysis.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who take that extra step.
We think you need these skills to ace Senior Applied AI Engineer
Some tips for your application 🫡
Show Your Passion for AI: When you're writing your application, let your enthusiasm for AI shine through! We want to see that you’re not just ticking boxes but genuinely excited about building systems that make a difference in fraud detection.
Tailor Your Experience: Make sure to highlight your relevant experience with Python, LLMs, and AWS. We love seeing how your past projects align with what we do at Veridox, so don’t hold back on those details!
Be Clear and Concise: Keep your application straightforward and to the point. We appreciate clarity, so avoid jargon and focus on what makes you a great fit for the role. Remember, less is often more!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss any important updates from our team!
How to prepare for a job interview at Veridox
✨Know Your Tech Inside Out
Make sure you’re well-versed in Python, AWS, and the specific AI technologies mentioned in the job description. Brush up on your experience with LLMs and VLMs, as well as any frameworks like LangChain. Being able to discuss your past projects and how you’ve tackled similar challenges will show that you’re not just a talker but a builder.
✨Showcase Your Problem-Solving Skills
Prepare to discuss real-world scenarios where you’ve debugged edge cases or improved systems. Think of examples where you’ve had to reason about complex evidence or made architectural decisions. This will demonstrate your critical thinking and ability to handle ambiguity, which is key for this role.
✨Understand the Domain
While you don’t need to be an expert in insurance fraud or document forensics, having a basic understanding of these areas can set you apart. Research how fraud detection works and be ready to discuss how AI can improve these processes. This shows your interest in the field and willingness to learn.
✨Ask Insightful Questions
Prepare thoughtful questions about the team’s current challenges, the technology stack, or the company’s approach to security. This not only shows your enthusiasm for the role but also helps you gauge if the company culture aligns with your values. Remember, interviews are a two-way street!