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

Apply Now

Sr. Machine Learning Engineer London, UK

Galytix Limited
London
3 weeks ago
Applications closed

Related Jobs

View all jobs

Sr. Machine Learning Engineer

Sr. Data Engineer (AWS / Python)

Data Engineer - GCP services & DBT

Sr. Data Scientist, GenAI Algorithms (Based in Dubai)

AI & Data Scientist

AI Sr. Data Engineer

Galytix (GX) is delivering on the promise of AI.

GX has built specialised knowledge AI assistants for the banking and insurance industry. Our assistants are fed by sector-specific data and knowledge and easily adaptable through ontology layers to reflect institution-specific rules.

GX AI assistants are designed for Individual Investors, Credit and Claims professionals. Our assistants are being used right now in global financial institutions. Proven, trusted, non-hallucinating, our assistants are empowering financial professionals and delivering 10x improvements by supporting them in their day-to-day tasks.

As a Sr. Machine Learning Engineer, you will need to:

  • Develop a state of the art data science and ML runtime stack in a multi-cloud environment.
  • Lead on software engineering and software design for ML components.
  • Understand and use computer science fundamentals, including data structures, algorithms, computability and complexity, and computer architecture.
  • Manage the infrastructure and pipelines needed to bring models and code into production.
  • Demonstrate end-to-end understanding of applications (including, but not limited to, the machine learning algorithms) being created.
  • Build algorithms based on statistical modelling procedures and maintain scalable machine learning solutions in production.
  • Apply machine learning algorithms and libraries.
  • Research and implement best practices to improve the existing machine learning infrastructure.
  • Collaborate with data engineers, application programmers, and data scientists.

Desired skills:

  • Qualification in a related field such as computer science, statistics, electrical engineering, mathematics, or physical sciences.
  • Self-starter with excellent communication and time management skills.
  • Strong computer programming skills, with knowledge of Python, R, and Java.
  • Experience scaling machine learning on data and compute grids.
  • Proficiency with Kubernetes, Docker, Linux, and cloud computing.
  • Experience with Dask, Airflow, and MLflow.
  • MLOps, CI, Git, and Agile processes.

Why you do not want to miss this career opportunity?

  • We are a mission-driven firm that is revolutionising the Insurance and Banking industry. We are not aiming to incrementally push the current boundaries; we redefine them.
  • Customer-centric organisation with innovation at the core of everything we do.
  • Capitalize on an unparalleled career progression opportunity.
  • Work closely with senior leaders who have individually served several CEOs in Fortune 100 companies globally.
  • Develop highly valued skills and build connections in the industry by working with top-tier Insurance and Banking clients on their mission-critical problems and deploying solutions integrated into their day-to-day workflows and processes.


#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.