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

Apply Now

Machine Learning Engineer

Allianz Popular SL.
Guildford
7 months ago
Applications closed

Related Jobs

View all jobs

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Allianz is a global insurance company serving across 70 different countries, but from the very first day you join our team you’ll know that your contributions are valued. We offer world class learning and career development opportunities, while we celebrate an inclusive culture.

As a Machine Learning Engineer at Allianz Commercial, you will work closely with our team of data scientists to operationalize ML models and ensure their scalability, reliability, and performance. Your expertise in MLOps and experience with the Azure cloud environment will be essential in designing and implementing end-to-end ML solutions. You will collaborate with cross-functional teams to identify ML requirements, develop ML pipelines, and deploy ML models into production. This role requires strong technical skills, a solid understanding of ML algorithms and deployment processes, and experience with MLOps best practices.

Responsibilities

  • Collaborate with data scientists and data engineers to understand ML requirements, and design and develop ML pipelines for data pre-processing, model training, model evaluation and model monitoring.
  • Build, optimize, and deploy ML models on the Azure cloud environment, leveraging Azure Machine Learning services and tools.
  • Develop automated ML workflows and implement CI/CD practices to ensure smooth model deployment and retraining.
  • Model serving/inferencing from data apps like Streamlit and/or Dash Plotly or Flask, Angular applications.
  • Design, deploy and manage APIs for model serving and leverage external APIs.
  • Implement monitoring and logging systems to track model performance, detect anomalies, and ensure model reliability and availability.
  • Collaborate with DevOps and IT teams to deploy ML models into production environments, ensuring scalability and security.
  • Develop and maintain ML infrastructure, including version control systems, model repositories, and experiment tracking platforms.
  • Implement ML model testing frameworks and ensure comprehensive model validation and evaluation.
  • Stay up to date with the latest Azure cloud services, MLOps techniques, tools, and frameworks, and proactively recommend improvements to enhance our ML operations.
  • Document and communicate ML engineering processes, best practices, and guidelines to ensure the reproducibility and maintainability of ML solutions.
  • Collaborate with cross-functional teams to define project goals, requirements, and success metrics.
  • Mentor and provide guidance to junior team members, fostering their growth and development in ML engineering.

About you

  • Bachelor's or Master’s degree in Computer Science, Data Science, or a related field. Equivalent experience will also be considered.
  • Proven experience as a Machine Learning Engineer or similar role, with a focus on ML model development and deployment in production environments.
  • Strong understanding of ML algorithms, deep learning frameworks (e.g., TensorFlow, PyTorch), and ML model architectures.
  • Experience with MLOps practices and tools, such as Docker, Kubernetes, Jenkins, or similar.
  • Proficiency in programming languages such as Python, and experience with ML libraries and frameworks.
  • Hands-on experience with the Azure cloud environment leveraging Azure ML, Cognitive Search, AKS, Synapse, Databricks, functions and pipelines.
  • Familiarity with version control systems (e.g., Git) and CI/CD practices.
  • Knowledge of Apigee, API gateway management or FastAPI desirable.
  • Knowledge of software engineering best practices, including code review, testing, and documentation.
  • Identify and assist in leveraging of new, potential areas of technology, industry best practices that can be adopted to solve business problems.
  • Strong problem-solving skills and ability to handle complex ML engineering challenges.
  • Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams.

What we will offer you

Recognised and rewarded for a job well done, we have a range of flexible benefits for you to choose from- so you can pick a package that’s perfect for you. We also offer flexible working options, global career opportunities across the wider Allianz Group, and fantastic career development and training. That’s on top of enjoying all the benefits you’d expect from the world’s number one insurance brand, including:

  • Contributory pension scheme
  • Life cover
  • Group Income Protection
  • Flexible buy/sell holiday options
  • Flexible working arrangements
  • A discount up to 50% on a range of insurance products including car, home and pet

Our ways of working

Do you need some flexibility with the hours you work? Let us know as part of your application and if it’s right for our customers, our business and for you, then we’ll do everything we can to make it happen.

Here at Allianz, we are signatories of the ABIs flexible working charter. We believe in supporting hybrid work patterns, which balance the needs of our customers, with your personal circumstances and our business requirements. Our aim with this is to help innovation, creativity, and you to thrive - Your work life balance is important to us.

Our Purpose and Values

We secure your future

Be Brave | With Heart | Everyone Counts | Inspiring Trust

Our purpose and values are more than just words on a website - they are the why and how of Allianz. They influence everything we do and guide us how to do it. Created by our people, for our people, they shape our culture, bring us together, and inspire us to be the best. Building an inclusive culture for us all to succeed.

Diversity & Inclusion

At Allianz, we value diversity and inclusion and back this up with our accreditations. Allianz is EDGE certified for gender inclusion, members of the Women in Finance Charter, members of the Stonewall Diversity Champion programme, signatories of Business in the Community’s Race at Work Charter, and an Armed Forces Covenant gold standard employer.

We have a range of employee networks focusing on gender inclusion, cultural diversity, LGBTQIA+, disability and long term health conditions (including neurodiversity), intergenerational and life stages, parents and carers, mental wellbeing, menopause support and armed forces and veterans, all supporting you to bring your best and authentic self to work.

#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.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.

Machine Learning Team Structures Explained: Who Does What in a Modern Machine Learning Department

Machine learning is now central to many advanced data-driven products and services across the UK. Whether you work in finance, healthcare, retail, autonomous vehicles, recommendation systems, robotics, or consumer applications, there’s a need for dedicated machine learning teams that can deliver models into production, maintain them, keep them secure, efficient, fair, and aligned with business objectives. If you’re hiring for or applying to ML roles via MachineLearningJobs.co.uk, this article will help you understand what roles are typically present in a mature machine learning department, how they collaborate through project lifecycles, what skills and qualifications UK employers look for, what the career paths and salaries are, current trends and challenges, and how to build an effective ML team.