MLOps Engineer

Aveni
Edinburgh
3 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist (Document Search)

Data Scientist

Principal Data Scientist - Marketing

Associate Director, Data Science and Innovation (Basé à London)

Data Science Manager

Data Science Manager

This is a remote position.

About the Company - Aveni is an award-winning technology company revolutionising the financial services industry. We utilise advanced AI to deliver scalable efficiency, leveraging cutting-edge Natural Language Processing (NLP) and Large Language Model (LLM) expertise. Our deep financial services domain knowledge allows us to drive unparalleled productivity and compliance for our clients. Having secured series A funding in July, our team is expanding fast, with strong growth plans predicted over the next few years.

About the Role - We are looking for a skilled and experienced MLOps Engineer to design, implement, and optimise machine learning infrastructure. You’ll be crucial in managing the lifecycle of ML models, from deployment to monitoring and maintenance, in a collaborative, fast-paced environment.


Responsibilities

  • Develop, deploy, and maintain scalable MLOps pipelines to automate key workflows.
  • Ensure solutions are platform-independent and support multi-cloud environments.
  • Use Infrastructure-as-Code (IaC) tools like Terraform or CloudFormation for automated deployments.
  • Collaborate with data scientists, engineers, and other teams to create optimised, production-ready solutions.
  • Deploy and orchestrate ML models using Docker, Kubernetes, and other tools; experience with deploying large-scale LLMs is necessary.
  • Implement monitoring and logging for ML models, ensuring robust alert systems and dashboards for model health and performance.
  • Optimise CI/CD pipelines for ML models to enhance speed and reliability.
  • Develop and enforce best practices for MLOps, including versioning and scalable deployments.
  • Support the transition from AWS to a multi-cloud environment while ensuring compatibility and reliability.


Requirements

  • Demonstrated experience in MLOps or related fields focusing on production-level ML deployment.
  • Hands-on experience with AWS, Azure, GCP, and platform-agnostic cloud solutions.
  • Proficiency with Docker, Kubernetes, and IaC tools like Terraform.
  • Experience with CI/CD pipelines using GitLab CI/CD, Jenkins, etc.
  • Strong understanding of ML model lifecycle management.
  • Familiarity with popular ML frameworks (e.g., TensorFlow, PyTorch).
  • Proficient scripting skills in Python, Bash, or similar.

Preferred Skills

  • Experience transitioning from AWS to a multi-cloud setup.
  • Familiarity with cloud-native storage and data engineering workflows.
  • Understanding of distributed systems and high-performance computing.


Benefits


  • 34 days of holiday plus your birthday off
  • Career progression opportunities
  • Share options
  • Flexible and remote working
  • Ongoing career development and training
  • Access to perks like free coffee, movie downloads, and high-street discounts
  • Comprehensive employee assistance program for emotional wellbeing
  • Access to a fitness portal and gym discounts
  • Pension scheme

<span class="font" style="font-family:-apple-system, system-ui, "system-ui", "Segoe UI", Roboto, "Helvetica Neue", "Fira Sans", Ubuntu, Oxygen, "Oxygen Sans", Cantarell, "Droid Sans", "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Lucida Grande", Helvetica, Arial, sans-serif">Join Us in Making a Difference - At Aveni, we value diversity and believe it fuels innovation. We are dedicated to building an inclusive team where everyone is empowered to contribute. If you're excited to leverage technology to impact financial services positively, we encourage you to apply—even if you don’t meet every requirement. Take the next step in your career and join Aveni in transforming the future of financial services with AI. Apply now to be part of our journey!


Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Portfolio Projects That Get You Hired for Data Science Jobs (With Real GitHub Examples)

Data science is at the forefront of innovation, enabling organisations to turn vast amounts of data into actionable insights. Whether it’s building predictive models, performing exploratory analyses, or designing end-to-end machine learning solutions, data scientists are in high demand across every sector. But how can you stand out in a crowded job market? Alongside a solid CV, a well-curated data science portfolio often makes the difference between getting an interview and getting overlooked. In this comprehensive guide, we’ll explore: Why a data science portfolio is essential for job seekers. Selecting projects that align with your target data science roles. Real GitHub examples showcasing best practices. Actionable project ideas you can build right now. Best ways to present your projects and ensure recruiters can find them easily. By the end, you’ll be equipped to craft a compelling portfolio that proves your skills in a tangible way. And when you’re ready for your next career move, remember to upload your CV on DataScience-Jobs.co.uk so that your newly showcased work can be discovered by employers looking for exactly what you have to offer.

Data Science Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Data science has become one of the most sought‑after fields in technology, leveraging mathematics, statistics, machine learning, and programming to derive valuable insights from data. Organisations across every sector—finance, healthcare, retail, government—rely on data scientists to build predictive models, understand patterns, and shape strategy with data‑driven decisions. If you’re gearing up for a data science interview, expect a well‑rounded evaluation. Beyond statistics and algorithms, many roles also require data wrangling, visualisation, software engineering, and communication skills. Interviewers want to see if you can slice and dice messy datasets, design experiments, and scale ML models to production. In this guide, we’ll explore 30 real coding & system‑design questions commonly posed in data science interviews. You’ll find challenges ranging from algorithmic coding and statistical puzzle‑solving to the architectural side of building data science platforms in real‑world settings. By practising with these questions, you’ll gain the confidence and clarity needed to stand out among competitive candidates. And if you’re actively seeking data science opportunities in the UK, be sure to visit www.datascience-jobs.co.uk. It’s a comprehensive hub featuring junior, mid‑level, and senior data science vacancies—spanning start‑ups to FTSE 100 companies. Let’s dive into what you need to know.

Negotiating Your Data Science Job Offer: Equity, Bonuses & Perks Explained

Data science has rapidly evolved from a niche specialty to a cornerstone of strategic decision-making in virtually every industry—from finance and healthcare to retail, entertainment, and AI research. As a mid‑senior data scientist, you’re not just running predictive models or generating dashboards; you’re shaping business strategy, product innovation, and customer experiences. This level of influence is why employers are increasingly offering compensation packages that go beyond a baseline salary. Yet, many professionals still tend to focus almost exclusively on base pay when negotiating a new role. This can be a costly oversight. Companies vying for data science talent—especially in the UK, where demand often outstrips supply—routinely offer equity, bonuses, flexible work options, and professional development funds in addition to salary. Recognising these opportunities and effectively negotiating them can have a substantial impact on your total earnings and long-term career satisfaction. This guide explores every facet of negotiating a data science job offer—from understanding equity structures and bonus schemes to weighing crucial perks like remote work and ongoing skill development. By the end, you’ll be well-equipped to secure a holistic package aligned with your market value, your life goals, and the tremendous impact you bring to any organisation.