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

Apply Now

Full Stack Java Software Engineer II

JPMorgan Chase & Co.
Glasgow
1 year ago
Applications closed

Related Jobs

View all jobs

Software Engineer II, Applied Machine Learning

Machine Learning Engineer II, Intelligent Talent Acquisition - Lead Generation & Detection Services

Machine Learning Software Engineer - Full Stack (LLM) in London - Aquent

PH6421-Full Stack Developer (with Data Engineering & LLM Training Experience)

Engineering Lead I - Machine Learning Platform

AI Data Engineer

You’re ready to gain the skills and experience needed to grow within your role and advance your career — and we have the perfect software engineering opportunity for you.

As a Full Stack Java Software Engineer II at JPMorgan Chase within the Corporate Technology, Compliance Controls and Regulatory Technology, you are part of an agile team that works to enhance, design, and deliver the software components of the firm’s state-of-the-art technology products in a secure, stable, and scalable way. As an emerging member of a software engineering team, you execute software solutions through the design, development, and technical troubleshooting of multiple components within a technical product, application, or system, while gaining the skills and experience needed to grow within your role.

Job responsibilities

Executes standard software solutions, design, development, and technical troubleshooting. Writes secure and high-quality code using the syntax of at least one programming language with limited guidance. Designs, develops, codes, and troubleshoots with consideration of upstream and downstream systems and technical implications. Applies knowledge of tools within the Software Development Life Cycle toolchain to improve the value realized by automation. Applies technical troubleshooting to break down solutions and solve technical problems of basic complexity. Gathers, analyzes, and draws conclusions from large, diverse data sets to identify problems and contribute to decision-making in service of secure, stable application development. Learns and applies system processes, methodologies, and skills for the development of secure, stable code and systems. Adds to team culture of diversity, equity, inclusion, and respect. Works under self-initiative and self-awareness. 

Required qualifications, capabilities, and skills

Formal training or certification in computer science or engineering concepts and expanding applied experience.  Good hands on experience in Java/J2EE (Version 8+), Cloud Native Micro Services and Angular JS/React JS. Hands on experience in Unit Testing (., JUnit, Mockito, Jasmin) Experience in Spring Core, Spring AOP, Spring Integration and Spring Data, Hibernate  Experience in design and developing APIs with best standards. Familiarity with CI/CD pipelines and modern deployment strategies and tools like GitHub, Jenkins.

Preferred qualifications, capabilities, and skills

Good knowledge in SQL/No-SQL databases like Oracle, Cassandra, S3 Familiarity with modern front-end technologies Emerging knowledge of software applications and technical processes within a technical discipline (., cloud, artificial intelligence, machine learning Exposure to different market-leading technologies like Kubernetes, Kafka, Elastic Search, Graph DB, GraphQL Knowledge in Document Management tools like FileNet.

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

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.

Jobs

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.

Jobs

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.

🍪 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.