Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Machine Learning Operations Engineer

Somerset Bridge Group
Bristol
6 days ago
Applications closed

Related Jobs

View all jobs

Lead AWS Data Engineer / Architect - Databricks - London

Lead Data Engineer

Azure Data Engineer - Manchester

Azure Data Engineer - Manchester

Senior Data Engineer

Machine Learning Computer Vision Engineer

The Machine Learning Operations Engineer will support the development and maintenance of the ML Ops platform for Project Pegasus. This role involves building and maintaining the data infrastructure required for the platform, developing API services, and ensuring the integration of ML models into the live environment.

Responsibilities:

  • Develop and maintain API services using Databricks and Azure.
  • Implement and manage Azure Cache (Redis) and Azure Redis.
  • Utilize Databricks Delta Live tables for data processing and analytics.
  • Integrate the platform with Snowflake for data storage and retrieval.
  • Collaborate with cross-functional teams to deliver the platform in an agile manner.
  • Ensure the platform supports offline analytics, ML models, lookup tables, and pricing actions.
  • Conduct load, end-to-end, and performance testing.
  • Produce pipeline code for running ML Ops jobs and create an Azure DevOps (GitHub) process for source control and deployment.

Requirements:

  • Experience in ML Ops engineering, with a focus on Azure and Databricks.
  • Knowledge of Postgres, Azure Cache (Redis), and Azure Redis.
  • Experience with Databricks Delta Live tables and Snowflake.
  • Experience in Data (Delta) Lake Architecture.
  • Experience with Docker and Azure Container Services.
  • Familiarity with API service development and orchestration.
  • Strong problem-solving skills and ability to work collaboratively.
  • Good communication skills and ability to work with cross-functional teams.
  • Experience with Azure Functions/Containers and Insights (not essential).
  • Experience in Software Development Life Cycle.

Additional Benefits:

  • Hybrid working: 2 days in the office and 3 days remote.
  • 25 days annual leave, increasing to 27 days after 2 years, and 30 days after 5 years, plus bank holidays.
  • Discretionary annual bonus.
  • Pension scheme: 5% employee, 6% employer.
  • Flexible working options and flexi-time.
  • Healthcare Cash Plan for healthcare costs.
  • Electric vehicle salary sacrifice scheme.
  • Exclusive retailer discounts.
  • Wellbeing, health & fitness app - Wrkit.
  • Enhanced parental leave, including IVF support.
  • Religious bank holidays for flexible use.
  • Life Assurance: 4x salary.
  • Car and Travel Insurance discounts.
  • Cycle to Work Scheme.
  • Employee Referral Scheme.
  • Community support day.


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

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

Industry Insights

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

Jobs

Top 10 Skills in Machine Learning According to LinkedIn & Indeed Job Postings

Machine learning (ML) is at the forefront of innovation, powering systems in finance, healthcare, retail, logistics, and beyond in the UK. As organisations leverage ML for predictive analytics, automation, and intelligent systems, demand for skilled practitioners continues to grow. So, which skills are most in demand? Drawing on insights from LinkedIn and Indeed, this article outlines the Top 10 machine learning skills UK employers are looking for in 2025. You'll learn how to demonstrate these capabilities through your CV, interviews, and real-world projects.

Careers

The Future of Machine Learning Jobs: Careers That Don’t Exist Yet

Machine learning (ML) has become one of the most powerful forces reshaping the modern world. From voice assistants and recommendation engines to fraud detection and medical imaging, it underpins countless applications. ML is no longer confined to research labs—it powers business models, public services, and consumer technologies across the globe. In the UK, demand for machine learning professionals has risen dramatically. Organisations in finance, retail, healthcare, and defence are embedding ML into their operations. Start-ups in Cambridge, London, and Edinburgh are pioneering innovations, while government-backed initiatives aim to position the UK as a global AI leader. Salaries for ML engineers and researchers are among the highest in the tech sector. Yet despite its current importance, machine learning is only at the beginning of its journey. Advances in generative AI, quantum computing, robotics, and ethical governance will reshape the profession. Many of the most vital machine learning jobs of the next two decades don’t exist today. This article explores why new careers will emerge, the roles likely to appear, how today’s roles will evolve, why the UK is well positioned, and how professionals can prepare now.

Jobs

Seasonal Hiring Peaks for Machine Learning Jobs: The Best Months to Apply & Why

The UK's machine learning sector has evolved into one of Europe's most intellectually stimulating and financially rewarding technology markets, with roles spanning from junior ML engineers to principal machine learning scientists and heads of artificial intelligence research. With machine learning positions commanding salaries from £32,000 for graduate ML engineers to £160,000+ for senior principal scientists, understanding when organisations actively recruit can dramatically accelerate your career progression in this pioneering and rapidly evolving field. Unlike traditional software engineering roles, machine learning hiring follows distinct patterns influenced by AI research cycles, model development timelines, and algorithmic innovation schedules. The sector's unique combination of mathematical rigour, computational complexity, and real-world application requirements creates predictable hiring windows that strategic professionals can leverage to advance their careers in developing tomorrow's intelligent systems. This comprehensive guide explores the optimal timing for machine learning job applications in the UK, examining how enterprise AI strategies, academic research cycles, and deep learning initiatives influence recruitment patterns, and why strategic timing can determine whether you join a groundbreaking AI research team or miss the opportunity to develop the next generation of machine learning algorithms.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.