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- Tasks: Design and build systems for real-time AI conversations in contact centres.
- Company: Join a cutting-edge tech company revolutionising conversational AI.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Collaborate with a dynamic team and tackle exciting technical challenges.
- Why this job: Make a real impact by optimising AI systems for high-stakes environments.
- Qualifications: Strong Python skills and experience with production backend systems required.
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 Engineer (AI Systems) in Manchester employer: ConnexAI
Contact Detail:
ConnexAI Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer (AI Systems) in Manchester
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to AI systems or machine learning. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice coding challenges and be ready to discuss your past experiences with real-time systems and scalability.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals like you. Plus, it’s a great way to ensure your application gets seen by the right people.
We think you need these skills to ace Machine Learning Engineer (AI Systems) 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 shows you’re keen on joining our team at StudySmarter!
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, APIs, and orchestration tools like Docker and Kubernetes. Being able to discuss your experience with these technologies confidently will show that you’re ready to tackle the challenges of the role.
✨Demonstrate Problem-Solving Skills
Prepare to discuss real-world scenarios where you've solved complex technical problems, especially related to scalability and latency. Think of examples where you improved system performance or handled failures effectively. This will highlight your ability to reason about concurrency and resource management.
✨Showcase Your Collaborative Spirit
Since this role involves working closely with data scientists and engineers, be ready to talk about your teamwork experiences. Share examples of how you’ve collaborated on projects, particularly in building or optimising production systems. This will demonstrate that you can thrive in a team-oriented environment.
✨Ask Insightful Questions
Prepare thoughtful questions about the company’s current AI systems and their future plans. Inquire about the challenges they face with real-time AI conversations and how they measure success. This shows your genuine interest in the role and helps you understand if it’s the right fit for you.