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For Companies At a Glance
- Tasks: Automate ML workflows and deploy cutting-edge models in production environments.
- Company: Join Aveni, a leader in AI for financial services.
- Benefits: 34 days holiday, share options, remote work, and continuous learning opportunities.
- Why this job: Make a real impact in the financial sector with innovative AI solutions.
- Qualifications: Experience in DevOps or MLOps, especially with AWS, Docker, and Kubernetes.
- Other info: Diverse and inclusive workplace with excellent career growth potential.
The predicted salary is between 28800 - 48000 £ per year.
Do you want to work on real-world deployments of cutting-edge machine learning models, including Large Language Models (LLMs)?
Aveni is redefining how AI works for financial services. Our purpose-built, vertically integrated platform combines proprietary Language Models (FinLLM), intelligent AI Agents, and targeted solutions like Aveni Assist and Aveni Detect. We partner with leading financial institutions to deliver scalable, secure, and compliant AI-powered solutions that create measurable value across advice, compliance, and operations.
We\’re looking for a motivated DevOps Engineer, eager to help us deploy and maintain machine learning models, especially Large Language Models (LLMs), in production environments. If you have solid DevOps experience and are keen to grow your MLOps skills, we'd love to hear from you!
What You\’ll Be Doing:
- Automating Machine Learning workflows (training → deployment) with AWS & GitOps
- Deploying LLMs using Kubernetes & Docker
- Building infrastructure with Terraform & Helm
- Monitoring and maintaining ML models with performance alerts and dashboards
- Supporting CI/CD for ML pipelines
- Developing production-grade APIs (REST/gRPC) to serve models
- Collaborating with engineers, data scientists & DevOps teams
Your Experience:
- Industry experience in DevOps or MLOps roles (ideally in AWS environments)
- Hands-on with Docker, Kubernetes, and Terraform
- Strong scripting skills in Python or Bash
- Familiar with ML lifecycle tools, model monitoring, and versioning
- Exposure to tools like KServe, Ray Serve, Triton, or vLLM a big plus
Bonus Points:
- Experience with observability frameworks like Prometheus or OpenTelemetry
- Knowledge of ML libraries: TensorFlow, PyTorch, HuggingFace
- Exposure to Azure or GCP
- Passion for financial services
Requirements:
- Degree in Computer Science, Engineering, Data Science or similar
What We Offer
- A collaborative and innovative work environment with awesome career growth opportunities
- 34 days holiday plus your birthday off (inclusive of bank holidays)
- Share options – we believe in shared success
- Skills development – continuous learning is at our core, expect the development to be front and centre of everything you do
- Remote and flexible working – remote, co-working spaces, or a mix of both
- Life insurance, income protection and private health care
- Freebies and discounts at a range of retailers
- Emotional wellbeing (Employee assistance programme provides access to 24/7 employee counselling and emotional support)
- Cycle to work scheme
- Pension scheme (employer contribution matched up to 5%)
Join Us in Making a Difference
At Aveni, we believe that diversity drives innovation. We\’re committed to building a team that reflects the diverse communities we serve and creating an inclusive workplace where everyone feels valued and empowered to contribute their best work. If you\’re passionate about leveraging technology to drive positive change and want to be part of a team that\’s shaping the future of financial services, we\’d love to hear from you. We know that some people are likely to only apply where they meet 100% of requirements, but we'd like to hear from you anyway. Apply now to join us on our mission to transform the financial services industry through AI!
DevOps Engineer employer: Aveni
Contact Detail:
Aveni Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land DevOps Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. A friendly chat can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! Create a GitHub repo with projects that highlight your DevOps and MLOps expertise. It’s a great way to demonstrate what you can do beyond the application.
✨Tip Number 3
Prepare for the interview by brushing up on your technical knowledge and soft skills. Practice common DevOps scenarios and be ready to discuss how you’d tackle real-world problems.
✨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 hearing from passionate candidates like you!
We think you need these skills to ace DevOps Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your DevOps experience and any MLOps skills you have. Use keywords from the job description to show that you're a perfect fit for the role.
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Share your passion for AI in financial services and explain why you want to work with us at Aveni. Be genuine and let your personality come through.
Showcase Relevant Projects: If you've worked on projects involving AWS, Docker, or Kubernetes, make sure to mention them. We love seeing real-world examples of your skills in action, especially if they relate to machine learning.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to see your application and get you into our hiring process quickly. Don’t miss out!
How to prepare for a job interview at Aveni
✨Know Your Tech Stack
Make sure you’re well-versed in the tools mentioned in the job description, like AWS, Docker, and Kubernetes. Brush up on your Terraform skills too! Being able to discuss how you've used these technologies in past projects will show that you're ready to hit the ground running.
✨Showcase Your MLOps Knowledge
Since this role focuses on deploying machine learning models, be prepared to talk about your experience with ML workflows. Discuss any specific projects where you automated processes or monitored model performance. This will demonstrate your understanding of the ML lifecycle and your ability to contribute effectively.
✨Prepare for Collaboration Questions
Collaboration is key in this role, so think of examples where you’ve worked closely with engineers or data scientists. Be ready to explain how you communicated technical concepts to non-technical team members, as this shows your ability to work in a diverse team environment.
✨Ask Insightful Questions
At the end of the interview, don’t forget to ask questions! Inquire about the team’s current projects or challenges they face with their ML models. This not only shows your interest in the role but also gives you insight into how you can make an impact from day one.
