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- Tasks: Design and build systems for real-time AI conversations in contact centres.
- Company: Innovative tech company focused on cutting-edge conversational AI solutions.
- Benefits: Competitive salary, flexible working hours, and opportunities for professional growth.
- Other info: Collaborate with experts in a dynamic environment with excellent career prospects.
- Why this job: Join a high-impact team and shape the future of AI technology.
- Qualifications: Strong Python skills and experience with production backend systems.
The predicted salary is between 60000 - 80000 £ per year.
We are building real-time conversational AI systems for contact centres, powered by ASR, LLMs, and TTS. As an LLM Systems Engineer, you will sit within our LLM team and focus on the systems layer that makes production Conversational AI work at scale. You’ll design and improve the infrastructure, orchestration, and runtime systems behind low-latency conversational AI workflows. This role focuses on solving the technical challenges associated with delivering real-time AI conversations: coordinating complex AI systems under strict latency and reliability constraints.
What you’ll do:
- Design and build systems that enable LLM workflows to maintain real-time responses even under peak load.
- Improve latency, throughput, concurrency, and reliability across our production systems.
- Build orchestration logic for model calls, services, queues, retries, fallbacks, and routing that balances load management with low response times.
- Help scale systems to support high volumes of concurrent real-time conversations.
- Optimise memory usage and resource efficiency across LLM-powered services.
- Deploy and support autoscaling in AI services running in AWS-based systems.
- Build observability into AI workflows, including monitoring, logging, alerting, and performance tracking.
- Work closely with data scientists, MLEs, prototype engineers, and backend engineers.
- Help turn LLM capabilities into stable, scalable production Conversational AI systems.
What we’re looking for:
- Strong Python engineering skills.
- Experience building production backend systems, distributed systems, or ML infrastructure.
- Strong understanding of scalability, latency, reliability, and performance engineering.
- Experience with cloud infrastructure, ideally AWS.
- Experience working with APIs, queues, service orchestration, and production monitoring.
- Understanding of how LLMs are used in production systems.
- Ability to reason about concurrency, throughput, memory usage, and failure handling.
- Strong debugging skills across complex production systems.
Nice to have:
- Experience with conversational AI, voice systems, ASR, TTS, or real-time streaming systems.
- Experience with model serving or inference infrastructure.
- Exposure to open-source LLMs or LLM orchestration frameworks.
- Experience with Docker, Kubernetes, ECS, or similar container orchestration tools.
- Experience with Redis, Kafka, Kinesis, SQS, or similar queueing/event systems.
- Familiarity with monitoring tools such as CloudWatch, Prometheus, or Grafana.
Why join? You’ll help build the systems behind real-time AI conversations used in production contact centre environments. This is a high-impact engineering role focused on low latency, scalability, reliability, and making LLM-powered systems work under real-world load.
Machine Learning Systems Engineer in Manchester employer: ConnexAI
Contact Detail:
ConnexAI Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Systems Engineer in Manchester
✨Tip Number 1
Network like a pro! Attend industry meetups, webinars, or tech conferences where you can connect with folks in the AI and machine learning space. You never know who might be looking for someone with your skills!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects related to LLMs, cloud infrastructure, or any relevant systems you've built. This gives potential employers a taste of what you can do beyond your CV.
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python and system design skills. Practice coding challenges and system architecture questions that focus on scalability and performance engineering.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals who can help us build real-time conversational AI systems. Your next big opportunity could be just a click away!
We think you need these skills to ace Machine Learning Systems Engineer in Manchester
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your Python engineering skills and experience with production backend systems. We want to see how your background aligns with the role, so don’t be shy about showcasing relevant projects or achievements!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about building real-time AI systems and how your skills can help us tackle the challenges of low-latency conversational AI. Keep it engaging and personal!
Showcase Your Technical Skills: When filling out your application, make sure to mention any experience you have with cloud infrastructure, especially AWS, and any tools like Docker or Kubernetes. We love seeing candidates who are familiar with the tech stack we use!
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’re considered for the role. Plus, it’s super easy to do!
How to prepare for a job interview at ConnexAI
✨Know Your Tech Inside Out
Make sure you’re well-versed in Python and the specific technologies mentioned in the job description. Brush up on your knowledge of AWS, Docker, and Kubernetes, as well as any relevant experience with APIs and orchestration tools. Being able to discuss these topics confidently will show that you’re ready to tackle the technical challenges of the role.
✨Demonstrate Problem-Solving Skills
Prepare to discuss real-world scenarios where you've solved complex problems related to scalability, latency, or reliability. Think of examples where you’ve optimised systems or improved performance under load. This will help interviewers see how you approach challenges and your ability to think critically about system design.
✨Show Your Collaborative Spirit
Since this role involves working closely with data scientists and engineers, be ready to talk about your experience in team settings. Share examples of how you’ve collaborated on projects, especially those involving LLMs or AI systems. Highlighting your teamwork skills will demonstrate that you can thrive in a collaborative environment.
✨Ask Insightful Questions
Prepare thoughtful questions about the company’s current projects, challenges they face with their conversational AI systems, or their tech stack. This not only shows your genuine interest in the role but also gives you a chance to assess if the company is the right fit for you. Engaging in a two-way conversation can leave a lasting impression.