KDB Developer

London
2 months ago
Applications closed

We are AMS. We are a global total workforce solutions firm; we enable organisations to thrive in an age of constant change by building, re-shaping, and optimising workforces. Our Contingent Workforce Solutions (CWS) is one of our service offerings; we act as an extension of our clients' recruitment team and provide professional interim and temporary resources.

Our investment banking client has been present in the UK for more than 150 years, they're a long-term partner to British business. Today, the Group is formed of 10 divisions and employs 9,300 staff based in 21 core locations right across the country. Their role is simply stated: help clients achieve their goals by combining local know-how and global reach. In so doing, they seek to make a positive, sustainable contribution to both the UK economy and society.

On behalf of this organisation, AMS are looking for a KDB Developer for an initial 6 month contract based in London with remote work available (Hybrid).

Purpose of the Role:

As a KDB developer you will play a crucial role in supporting the clients financial technology team by gaining hands-on experience in developing and maintaining KDB+/q applications.

As a KDB Developer you will be responsible for:

Collaborate with the development team to design, code, test, and implement KDB+/q solutions for efficient data processing and analytics.
Assist in the maintenance and enhancement of existing KDB applications, ensuring optimal performance and reliability.
Analyze and optimize KDB queries for improved performance and responsiveness.
Identify and address bottlenecks in data retrieval and processing.
Document code, processes, and procedures to ensure knowledge transfer and maintain a comprehensive record of development activities.
Collaborate with cross-functional teams, including data scientists, analysts, and business stakeholders, to understand requirements and deliver effective solutions.What we require from the candidate:

Familiarity with KDB+/q technologies.
Strong analytical and problem-solving skills.
Experienced of analysing and presenting data for end-users and applications.Next steps

If you are interested in applying for this position and meet the criteria outlined above, please click the link to apply and we will contact you with an update in due course.

This client will only accept workers operating via an Umbrella or PAYE engagement model.

AMS, a Recruitment Process Outsourcing Company, may in the delivery of some of its services be deemed to operate as an Employment Agency or an Employment Business

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.

Jobs

Portfolio Projects That Get You Hired for Machine Learning Jobs (With Real GitHub Examples)

In today’s data-driven landscape, the field of machine learning (ML) is one of the most sought-after career paths. From startups to multinational enterprises, organisations are on the lookout for professionals who can develop and deploy ML models that drive impactful decisions. Whether you’re an aspiring data scientist, a seasoned researcher, or a machine learning engineer, one element can truly make your CV shine: a compelling portfolio. While your CV and cover letter detail your educational background and professional experiences, a portfolio reveals your practical know-how. The code you share, the projects you build, and your problem-solving process all help prospective employers ascertain if you’re the right fit for their team. But what kinds of portfolio projects stand out, and how can you showcase them effectively? This article provides the answers. We’ll look at: Why a machine learning portfolio is critical for impressing recruiters. How to select appropriate ML projects for your target roles. Inspirational GitHub examples that exemplify strong project structure and presentation. Tangible project ideas you can start immediately, from predictive modelling to computer vision. Best practices for showcasing your work on GitHub, personal websites, and beyond. Finally, we’ll share how you can leverage these projects to unlock opportunities—plus a handy link to upload your CV on Machine Learning Jobs when you’re ready to apply. Get ready to build a portfolio that underscores your skill set and positions you for the ML role you’ve been dreaming of!

Jobs Careers

Machine Learning Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Machine learning is fuelling innovation across every industry, from healthcare to retail to financial services. As organisations look to harness large datasets and predictive algorithms to gain competitive advantages, the demand for skilled ML professionals continues to soar. Whether you’re aiming for a machine learning engineer role or a research scientist position, strong interview performance can open doors to dynamic projects and fulfilling careers. However, machine learning interviews differ from standard software engineering ones. Beyond coding proficiency, you’ll be tested on algorithms, mathematics, data manipulation, and applied problem-solving skills. Employers also expect you to discuss how to deploy models in production and maintain them effectively—touching on MLOps or advanced system design for scaling model inferences. In this guide, we’ve compiled 30 real coding & system‑design questions you might face in a machine learning job interview. From linear regression to distributed training strategies, these questions aim to test your depth of knowledge and practical know‑how. And if you’re ready to find your next ML opportunity in the UK, head to www.machinelearningjobs.co.uk—a prime location for the latest machine learning vacancies. Let’s dive in and gear up for success in your forthcoming interviews.

Jobs

Negotiating Your Machine Learning Job Offer: Equity, Bonuses & Perks Explained

How to Secure a Compensation Package That Matches Your Technical Mastery and Strategic Influence in the UK’s ML Landscape Machine learning (ML) has rapidly shifted from an emerging discipline to a mission-critical function in modern enterprises. From optimising e-commerce recommendations to powering autonomous vehicles and driving innovation in healthcare, ML experts hold the keys to transformative outcomes. As a mid‑senior professional in this field, you’re not only crafting sophisticated algorithms; you’re often guiding strategic decisions about data pipelines, model deployment, and product direction. With such a powerful impact on business results, companies across the UK are going beyond standard salary structures to attract top ML talent. Negotiating a compensation package that truly reflects your value means looking beyond the numbers on your monthly payslip. In addition to a competitive base salary, you could be securing equity, performance-based bonuses, and perks that support your ongoing research, development, and growth. However, many mid‑senior ML professionals leave these additional benefits on the table—either because they’re unsure how to negotiate them or they simply underestimate their long-term worth. This guide explores every critical aspect of negotiating a machine learning job offer. Whether you’re joining an AI-focused start-up or a major tech player expanding its ML capabilities, understanding equity structures, bonus schemes, and strategic perks will help you lock in a package that matches your technical expertise and strategic influence. Let’s dive in.

🍪 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.