At a Glance
- Tasks: Build and operate AI infrastructure, deploying systems in live environments.
- Company: LEC AI, a high-growth division of London Export Corporation.
- Benefits: Competitive salary, hands-on experience, and impactful projects.
- Other info: Fast-paced environment with minimal bureaucracy and visible impact on operations.
- Why this job: Join a small team and make a real impact on AI systems in production.
- Qualifications: 3+ years in production infrastructure, strong Python skills, cloud platform experience.
The predicted salary is between 40000 - 60000 € per year.
LEC AI is the intelligence systems division inside London Export Corporation, a London-headquartered group operating across AI, forecasting, robotics, logistics, commerce, and operational software. We are at a high-growth stage, scaling from founding team to production AI systems deployed across multiple businesses.
This role focuses on the deployment, reliability, scaling, and operational infrastructure underneath those systems.
The RoleWe are hiring an AI Deployment & Platform Engineer to build and operate the infrastructure layer powering our AI systems in production. You will work directly with the AI systems engineering team to deploy AI systems into live environments, manage runtime infrastructure, scale orchestration systems, optimise inference performance, and build the deployment pipelines and observability that keep everything running.
What You Will Build- Deployment Infrastructure
- Deploy and manage AI systems primarily across AWS and Azure, with Alibaba Cloud for China-based deployments and GCP as workloads require
- Containerise and orchestrate AI workloads at scale
- Build CI/CD pipelines for AI systems and model deployments
- Manage inference infrastructure and deployment automation
- Design scalable runtime environments for multi-agent systems
- Reliability and Scaling
- Monitor system performance, latency, throughput, and uptime
- Build observability, logging, and alerting systems
- Manage autoscaling and infrastructure optimisation
- Debug production failures and runtime bottlenecks
- Infrastructure Operations
- Monitor model drift, data drift, and runtime quality degradation
- Implement rollback, failover, and deployment safety systems
- Manage GPU infrastructure and workload scheduling
- Optimise model serving costs and cloud spend
You will support deployment and operations for organisational intelligence platforms, large-scale prediction systems, multi-agent workflows, multimodal AI systems, and future AI-native SaaS products.
Who You AreYou have 3+ years of experience operating production infrastructure under real-world conditions. You are highly hands-on and comfortable owning systems directly. You understand that AI systems are operational systems, and that reliability, latency, observability, and cost control matter as much as model quality. You write production code regularly. Python is expected.
Strong experience across the following is highly valuable:- containerisation and orchestration
- major cloud platforms (AWS, Azure)
- infrastructure-as-code
- backend API frameworks
- caching layers and in-memory data stores
- relational and vector databases
- workflow orchestration
- CI/CD pipelines
- GPU infrastructure
- monitoring and observability stacks
- inference optimisation
- model serving runtimes
- async and streaming systems
- MLOps tooling
- multi-agent systems
- Alibaba Cloud or other China cloud providers
- Built and operated AI systems in production
- Managed cloud infrastructure at scale
- Reduced infrastructure cost or inference latency significantly
- Built deployment automation pipelines
- Worked on real-time or high-throughput systems
- Strong debugging and systems instincts
- Comfortable in fast-moving environments with high ownership
- Mandarin is a bonus given our UK and China operations
This is a founding-stage infrastructure hire inside a high-growth AI division. The systems you deploy will run inside active businesses with real operational impact, not pilots that get shelved. You will work on multi-agent systems, orchestration runtimes, large-scale prediction systems, and real-time AI deployment.
Small team. Fast execution. Minimal bureaucracy. Good ideas move quickly into production. Your work is visible to the leadership team and shapes the platform from day one.
Location and EligibilityBased in Central London (Knightsbridge). Full-time, 5 days on-site. Salary: £40,000 to £60,000 GBP, depending on experience, with significant upside as LEC AI scales. We are unable to provide visa sponsorship for this role. Applicants must have the right to work in the UK.
How to ApplyApply on LinkedIn and email your portfolio to with the subject line: AI Deployment & Platform Engineer. Show us systems you have deployed, infrastructure you have operated, CI/CD pipelines you have built, GitHub, and any debugging or scaling problems you have solved.
AI Deployment & Platform Engineer in Slough employer: LEC AI
LEC AI offers a dynamic and innovative work environment in the heart of Central London, where employees are empowered to take ownership of their projects and contribute to impactful AI systems that operate in real business settings. With a strong focus on employee growth, LEC AI provides opportunities for hands-on experience in cutting-edge technology, alongside a collaborative culture that values creativity and quick execution. Join us to be part of a high-growth team that combines the agility of a startup with the stability of an established trading group, ensuring your contributions are recognised and rewarded.
StudySmarter Expert Advice🤫
We think this is how you could land AI Deployment & Platform Engineer in Slough
✨Tip Number 1
Get your hands dirty! When you’re applying for a role like AI Deployment & Platform Engineer, it’s crucial to showcase your practical experience. Share specific examples of systems you've deployed or infrastructure you've managed in your interviews.
✨Tip Number 2
Network like a pro! Connect with people in the industry on LinkedIn and attend relevant meetups. You never know who might have the inside scoop on job openings or can refer you directly to hiring managers.
✨Tip Number 3
Prepare for technical challenges! Brush up on your coding skills and be ready to solve real-world problems during interviews. Practice common scenarios related to cloud platforms, CI/CD pipelines, and system reliability.
✨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 are proactive about their job search!
We think you need these skills to ace AI Deployment & Platform Engineer in Slough
Some tips for your application 🫡
Show Us Your Experience:When you're applying, make sure to highlight your hands-on experience with production infrastructure. We want to see the real-world problems you've tackled and how you've made AI systems reliable at scale.
Be Specific About Your Skills:Don't just list your skills; give us examples of how you've used them. If you've containerised workloads or built CI/CD pipelines, tell us about it! We love seeing concrete achievements that demonstrate your expertise.
Tailor Your Application:Make your application stand out by tailoring it to our job description. Use the same language we do and connect your past experiences to the specific requirements of the AI Deployment & Platform Engineer role.
Apply Through Our Website:We encourage you to apply through our website for a smoother process. It helps us keep track of applications better and ensures you don’t miss any important updates from us!
How to prepare for a job interview at LEC AI
✨Know Your Tech Stack
Make sure you’re well-versed in the technologies mentioned in the job description, especially AWS, Azure, and containerisation tools. Brush up on your Python skills and be ready to discuss how you've used these technologies in real-world scenarios.
✨Showcase Your Problem-Solving Skills
Prepare examples of operational problems you've solved in the past. Be specific about the challenges you faced, the solutions you implemented, and the impact they had on system performance or cost efficiency.
✨Demonstrate Your Hands-On Experience
This role is all about being hands-on, so be ready to talk about your direct experience with deploying AI systems and managing infrastructure. Highlight any CI/CD pipelines you've built and how you’ve optimised inference performance in previous roles.
✨Ask Insightful Questions
Prepare thoughtful questions about the company’s current projects and future goals. This shows your genuine interest in the role and helps you understand how you can contribute to their high-growth stage effectively.
