Transaction Banking and Other - London - Vice President - Software Engineering London, Greater [...] (Basé à London)

Jobleads
London
1 month ago
Create job alert

WHAT WE DO

At Goldman Sachs, our Engineers don’t just make things – we make things possible. We change the world by connecting people and capital with ideas and solve the most challenging and pressing engineering problems for our clients. Our engineering teams build scalable software and systems, architect low latency infrastructure solutions, proactively guard against cyber threats, and leverage machine learning alongside financial engineering to continuously turn data into action.

Engineering, which is comprised of our Technology Division and global strategist groups, is at the critical center of our business. Our dynamic environment requires innovative strategic thinking. Want to push the limit of digital possibilities? Start here.

Who We Look For

Goldman Sachs Engineers are innovators and problem-solvers, building solutions in risk management, big data, mobile and more. We look for creative collaborators who evolve, adapt to change and thrive in a fast-paced global environment.

The RoleTransaction Banking:

Transaction Banking, a business unit within Platform Solutions, aims to provide comprehensive cash management solutions for corporations. Transaction Banking combines the strength and heritage of a 155+ year-old financial institution with the agility and entrepreneurial spirit of a tech start-up. Our goal is to provide the best client experience. Through the use of modern technologies centered on data and analytics, we provide customers with powerful tools that are grounded in value, transparency and simplicity to improve cash flow management efficiency.

The Team:

Financial Risk Engineering is a global team with presence in New York, London, Bengaluru and Dallas. We are responsible for the technical design and development of systems that protect the firm and our clients from Financial Crime including real-time Fraud prevention and compliance with global Sanctions and regulatory requirements, using existing and emerging technologies.

The Role:

Working in the Financial Risk Engineering team in London, you will be responsible for the development, testing and rollout of new features. You are expected to shape and implement our strategic vision for a variety of next-gen platforms that will protect the firm and our clients from Financial Crime, contributing to a world-class engineering culture, while integrating business value and client experience within the team. We expect a successful candidate to have excellent communication skills, deliver high quality software and to be passionate about cutting edge engineering systems. You must have a proficient understanding of software development concepts. A good understanding of the Cloud and Container concepts is a plus. You will also be responsible to develop supportable software and liaise with our SRE (Site Reliability Engineering) team to factor in their requirements.

RESPONSIBILITIES AND QUALIFICATIONS

HOW YOU WILL FULFILL YOUR POTENTIAL

  • Develop full stack applications with due consideration to security, design, validation and SDLC framework
  • Collaborate with product managers, business operations, engineers to define product requirements, objectives
  • Participate in system design consulting, platform management
  • Develop resilient, scalable and secure modules using cloud native services

BASIC QUALIFICATIONS

  • BS degree in Computer Science or related technical field involving programming or systems engineering.
  • Minimum 3 years of relevant professional experience using a modern programming language (preferably Java)
  • Proficiency in development with Java, springboot, REST APIs
  • Experience engineering solutions with distributed tracing, Performance testing, Authentication, Authorization
  • Proficiency with algorithms, data structures and software design
  • Experience with UNIX operating systems internals, infrastructure as code - Terraform and networking
  • Proven to work independently in a fast paced and often multi-direction work environment

PREFERRED QUALIFICATIONS

  • Experience with development and design of distributed systems
  • Experience in financial services specifically corporate cash management desirable
  • Strong communication skills, drive, and ownership
  • Experience with AWS services - Amazon MSK/Apache kafka, ECS/kubernetes, S3, IAM, AWS XRay
  • Basic knowledge of data science and machine learning is preferable but not essential

#J-18808-Ljbffr

Related Jobs

View all jobs

Technical Architect - AI Development - Director | London, UK (Basé à London)

CROSS BORDER GLOBAL BANKER EXECUTIVE DIRECTOR

SQL Data Analyst

Forensic Financial Data Analyst (Assistant Manager)

Chargeback Specialist

Assistant Manager - Pricing

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.

Machine‑Learning Jobs for Non‑Technical Professionals: Where Do You Fit In?

The Model Needs More Than Math When ChatGPT went viral and London start‑ups raised seed rounds around “foundation models,” many professionals asked, “Do I need to learn PyTorch to work in machine learning?” The answer is no. According to the Turing Institute’s UK ML Industry Survey 2024, 39 % of advertised ML roles focus on strategy, compliance, product or operations rather than writing code. As models move from proof‑of‑concept to production, demand surges for specialists who translate algorithms into business value, manage risk and drive adoption. This guide reveals the fastest‑growing non‑coding ML roles, the transferable skills you may already have, real transition stories and a 90‑day action plan—no gradient descent necessary.

Quantexa Machine‑Learning Jobs in 2025: Your Complete UK Guide to Joining the Decision‑Intelligence Revolution

Money‑laundering rings, sanctioned entities, synthetic identities—complex risks hide in plain sight inside data. Quantexa, a London‑born scale‑up now valued at US $2.2 bn (Series F, August 2024), solves that problem with contextual decision‑intelligence (DI): graph analytics, entity resolution and machine learning stitched into a single platform. Banks, insurers, telecoms and governments from HSBC to HMRC use Quantexa to spot fraud, combat financial crime and optimise customer engagement. With the launch of Quantexa AI Studio in February 2025—bringing generative AI co‑pilots and large‑scale Graph Neural Networks (GNNs) to the platform—the company is hiring at record pace. The Quantexa careers portal lists 450+ open roles worldwide, over 220 in the UK across data science, software engineering, ML Ops and client delivery. Whether you are a graduate data scientist fluent in Python, a Scala veteran who loves Spark or a solutions architect who can turn messy data into knowledge graphs, this guide explains how to land a Quantexa machine‑learning job in 2025.

Machine Learning vs. Deep Learning vs. MLOps Jobs: Which Path Should You Choose?

Machine Learning (ML) continues to transform how businesses operate, from personalised product recommendations to automated fraud detection. As ML adoption accelerates in nearly every industry—finance, healthcare, retail, automotive, and beyond—the demand for professionals with specialised ML skills is surging. Yet as you browse Machine Learning jobs on www.machinelearningjobs.co.uk, you may encounter multiple sub-disciplines, such as Deep Learning and MLOps. Each of these fields offers unique challenges, requires a distinct skill set, and can lead to a rewarding career path. So how do Machine Learning, Deep Learning, and MLOps differ? And which area best aligns with your talents and aspirations? This comprehensive guide will define each field, highlight overlaps and differences, discuss salary ranges and typical responsibilities, and explore real-world examples. By the end, you’ll have a clearer vision of which career track suits you—whether you prefer building foundational ML models, pushing the boundaries of neural network performance, or orchestrating robust ML pipelines at scale.