Lead Architect - Public Cloud

JPMorgan Chase & Co.
London
11 months ago
Applications closed

Related Jobs

View all jobs

Senior Solutions Architect

Principal Data Scientist - Generative AI

Principal Data Scientist - Generative AI

Lead Enterprise Architect, Advanced Analytics

Lead Data Scientist (GenAI)

Lead, Data Scientist (Deep Learning), Peacock Video Streaming Service

A career with us is a journey, not a destination. This could be the next best step in your technical career. Join us. 

As a Lead Architect at JPMorgan Chase within the Enterprise Technology and Infrastructure Platforms division, you are an integral part of a team that works to develop high-quality architecture solutions for various software applications on modern cloud-based technologies. As a core technical contributor, you are responsible for conducting critical architecture solutions across multiple technical areas within various business functions in support of project goals.

Job responsibilities

Provide consultation for line of business applications team on how to migrate their enterprise application to cloud using well-architected designs to maximize the capabilities of cloud computing Translates technical issues, trends, and approaches to leadership to drive the firm’s innovation and enable leaders to make strategic, well-informed decisions about target state architecture Engages technical teams and business stakeholders to discuss and propose technical approaches to meet current and future needs Evaluates recommendations and provides feedback on new technologies Executes creative software solutions, design, development, and technical troubleshooting with the ability to think beyond routine or conventional approaches to build solutions or break down technical problems Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information  Involve in the firm’s culture of diversity, equity, inclusion, and respect Assist adoption and implementation of technical methods in specialized fields in line with the latest product development methodologies Creates durable, reusable software frameworks that improves velocity and quality of output across teams and functions Coach, train, and mentor to improve the maturity and value of the Cloud practices 

 Required qualifications, capabilities, and skills

Formal training or certification on Cloud Architecture concepts and advanced applied experience Hands-on practical experience delivering system design, application development, testing, and operational stability Knowledge of major public cloud platforms, ideally with AWS Certified Solutions Architect Associate or equivalent professional level or specialty certifications Proficiency in automation and continuous delivery methods Proficient in all aspects of the Software Development Life Cycle Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security Demonstrated proficiency in software applications and technical processes within a technical discipline (., cloud, artificial intelligence, machine learning, mobile, In-depth knowledge of the financial services industry and their IT systems Practical cloud native experience Advanced knowledge of one or more software, applications, and architecture disciplines Ability to evaluate current and emerging technologies to recommend the best solutions for the future state architecture

Preferred qualifications, capabilities, and skills

Demonstrated experience in well-architected designs, application development, testing, resiliency and operational stability to maximize the capabilities of cloud computing for enterprise applications Technical architecture experience with demonstrated increases in responsibility.  Awareness and practical experience with implementing Resiliency and FinOps practices 

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 AI Jobs (With Real GitHub Examples)

In the fast-evolving world of artificial intelligence (AI), an impressive portfolio of projects can act as your passport to landing a sought-after role. Even if you’ve aced interviews in the past, employers in AI and machine learning (ML) are increasingly asking candidates to demonstrate hands-on experience through the projects they’ve built and shared online. This is because practical ability often speaks volumes about your suitability for a role—far more than any exam or certification alone could. In this article, we’ll explore how to build an outstanding AI portfolio that catches the eye of recruiters and hiring managers, including: Why an AI portfolio is crucial for job seekers. How to choose AI projects that align with your target roles. Specific project ideas and real GitHub examples to help you stand out. Best practices for showcasing your work, from writing clear READMEs to using Jupyter notebooks effectively. Tips on structuring your GitHub so that employers can instantly see your value. Moreover, we’ll discuss how you can use your portfolio to connect with top employers in AI, with a handy link to our CV-upload page on Artificial Intelligence Jobs for when you’re ready to apply. By the end, you’ll have a clear roadmap to building a portfolio that will help secure interviews—and the AI job—of your dreams.

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

In today's competitive AI job market, nailing a technical interview can be the difference between landing your dream role and getting lost in the crowd. Whether you're looking to break into machine learning, deep learning, NLP (Natural Language Processing), or data science, your problem-solving skills and system design expertise are certain to be put to the test. AI‑related job interviews typically involve a range of coding challenges, algorithmic puzzles, and system design questions. You’ll often be asked to delve into the principles of machine learning pipelines, discuss how to optimise large-scale systems, and demonstrate your coding proficiency in languages like Python, C++, or Java. Adequate preparation not only boosts your confidence but also reduces the likelihood of fumbling through unfamiliar territory. If you’re actively seeking positions at major tech companies or innovative AI start-ups, then check out www.artificialintelligencejobs.co.uk for some of the latest vacancies in the UK. Meanwhile, this blog post will guide you through 30 real coding & system-design questions you’re likely to encounter during your AI job interview. This list is designed to help you practise, anticipate typical question patterns, and stay ahead of the competition. By reading through each question and thinking about the possible approaches, you’ll sharpen your problem-solving skills, time management, and critical thinking. Each question covers fundamental concepts that employers regularly test, ensuring you’re well-equipped for success. Let’s dive right in.

Negotiating Your AI Job Offer: Equity, Bonuses & Perks Explained

Artificial intelligence (AI) has proven itself to be one of the most transformative forces in today’s business world. From smart chatbots in customer service to predictive analytics in finance, AI technologies are reshaping how organisations operate and innovate. As the demand for AI professionals grows, so does the complexity of compensation packages. If you’re a mid‑senior AI professional, you’ve likely seen job offers that include far more than just a base salary—think equity, bonuses, and a range of perks designed to entice you into joining or staying with a company. For many, the focus remains squarely on salary. While that’s understandable—after all, your monthly take‑home pay is what covers day-to-day expenses—limiting your negotiations to salary alone can leave considerable value on the table. From stock options in ambitious startups to sign‑on bonuses that ‘buy you out’ of your current contract, modern AI job offers often include elements that can significantly boost your long-term wealth and job satisfaction. This article aims to shed light on the full scope of AI compensation—specifically focusing on how equity, bonuses, and perks can enhance (or sometimes detract from) the overall value of your package. We’ll delve into how these elements work in practice, what to watch out for, and how to navigate the negotiation process effectively. Our goal is to provide mid‑senior AI professionals with the insights and tools to land a holistic compensation deal that accurately reflects their technical expertise, leadership potential, and strategic importance in this fast-moving field. Whether you’re eyeing a leadership role in machine learning at an established tech giant, or you’re considering a pioneering position at a disruptive AI startup, the knowledge in this guide will help you weigh the merits of base salary alongside the potential riches—and risks—of equity, bonuses, and other benefits. By the end, you’ll have a clearer sense of how to align your compensation with both your immediate lifestyle needs and long-term career aspirations.