Solution Architect

Windsor
2 months ago
Applications closed

Related Jobs

View all jobs

Data & AI Solution Architect, Azure, Remote

Microsoft Data Solution Architect

Senior Solutions Architect

Data Engineer - Python & Azure

Data Engineer - Python & Azure

Data Scientist, Generative AI Innovation Center

About your role:

We're looking for a Software Architect to lead the design and implementation of high-level software architectures, collaborating with cross-functional teams-including machine learning and data scientists and external product teams-to deliver innovative solutions aligned with business objectives. This role exists to ensure the development of scalable, efficient, and integrated software systems while leading the team to uphold best practices and achieve project goals. We're seeking an experienced and adaptable Software Architect with strong leadership abilities and expertise in collaborative, cloud-based software development, ideally in a corporate research environment.

The team is currently primarily in Antwerp, Belgium but we are building a Windsor, Berkshire-centred team to match. This is a hybrid working position, based in Windsor, Berkshire (2 days on-site). It will be useful to be able to travel between the locations.

Key responsibilities will include:

Solution Architecture and Design

Design and oversee high-level software architectures aligned with business objectives.

Apply data engineering principles to design efficient data pipelines and storage.

Integrate solutions seamlessly with machine learning models and data science workflows.

Technical Leadership and Collaboration

Collaborate closely with researcher engineers, data scientists, software and DevOps engineers, to ensure the quality of solutions.

Promote best practices within the team, including rigorous testing, code review, continuous integration/continuous deployment (CI/CD) techniques, and well-maintained documentation.

Introduce innovative solutions using emerging technologies.

Team Leadership and Development

Lead and mentor a team of developers, fostering excellence.

Enhance team skills in coding practices and technical competencies.

Conduct performance evaluations and support career growth.

Here's what we're looking for:

Professional experience

A bachelor's degree in computer science, Software Engineering, or a related field is required; a master's degree or relevant certifications (such as AWS Certified Solutions Architect) are highly desirable.

Professional experience in software development, including significant experience in software architecture and team leadership.

Proven ability to design high-level software architectures aligned with business goals.

Technical knowledge

Understanding of data architecture concepts and best practices to support our machine learning and data science activities.

Experience in architecting and managing scalable cloud-native solutions particularly on AWS.

Advanced knowledge of Python programming and relevant frameworks.

Bonus: Working knowledge of other languages, especially JVM-based, Go, or Rust.

Bonus: Familiarity with some of our other key applications, e.g. web or mobile front-end design, data persistency, IoT devices.

Leadership and Software Management

Experience with testing, code review practices, code deployment, and infrastructure management.

Proven ability to lead and mentor development teams effectively.

Experience conducting performance evaluations and supporting career growth.

Skilled in fostering a collaborative and high-performance team environment.

Communication and Collaboration

Excellent verbal and written communication skills.

Ability to collaborate closely with cross-functional teams and stakeholders.

Skilled in conveying complex technical concepts to non-technical audiences

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.