ICT Architect - Cyber Security

Boston Consulting Group
London
1 month ago
Applications closed

Related Jobs

View all jobs

Senior Teradata Engineer

Senior Data Engineer

Data Engineer - Power BI

Data Engineer

Boston Consulting Group partners with leaders in business and society to tackle their most important challenges and capture their greatest opportunities. Today, we help clients with total transformation-inspiring complex change, enabling organizations to grow, building competitive advantage, and driving bottom-line impact.To succeed, organizations must blend digital and human capabilities. BCG delivers solutions through leading-edge management consulting along with technology and design, corporate and digital ventures—and business purpose. Our New York-based North American team began in 2014 and in 2017 acquired MAYA Design, a Pittsburgh-based digital design and innovation lab, to grow our capabilities around technology and design. We support our clients’ total digital transformation through technology, design, cybersecurity, and risk management & financial engineering capabilities. And together with BCG, BCG Platinion’s interdisciplinary team of technical experts enable customized technical solutions and accelerate delivery value through new business platforms, application consolidations, and major system implementations.At BCG Platinion we're keen to engage with experienced, passionate and creative IT Architects who will help us unlock our client's digital potential and ignite change. We build unique solutions to the complexities our clients face, while providing our people with opportunities to explore and grow. A community of leading experts, BCG Platinion team members have a natural springboard for professional growth and deeper opportunities to excel. As an IT Architect you will work alongside a bold, energetic and collaborative team. You will evaluate and coach our clients' technology teams, define technology strategies, architecture solutions and help us design the future. Together with our clients, you will develop superior digital and technology concepts and architecture solutions as well as support technical implementations actively and on site, applying your sound technical know-how, your understanding of business contexts, and your analytical and conceptual skills.4 to 6 years' IT Architecture experience working in a software development, technical project management, digital delivery, or technology consulting environment• Platform implementation experience (Apache Hadoop - Kafka - Storm and Spark, Elasticsearch and others)• Experience around data integration & migration, data governance, data mining, data visualisation, database modelling in an agile delivery-based environment.• Microsoft Azure, Amazon Web Service (AWS) and/or Google Cloud Platform (GCP)• Management of work packages/modules in critical IT implementation projects• Technical architecture, code reviews & performance of technical proofs of concepts• Analysis of complex IT Application landscapes, optimization of development processes and Solution Architecture• Data lake experience on AWS and Azure• General knowledge of database technologies and trade-offs• University degree with above-average academic performance in a mathematical-scientific field, information technology, or business administrationAll qualified applicants will receive consideration for employment without regard to race, color, age, religion, sex, sexual orientation, gender identity / expression, national origin, disability, protected veteran status, or any other characteristic protected under national, provincial, or local law, where applicable, and those with criminal histories will be considered in a manner consistent with applicable state and local laws.

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.

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!

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.

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.