Technical Lead Software Engineer

Adecco
Greater London
1 month ago
Applications closed

Related Jobs

View all jobs

Software Engineer

Machine Learning and AI Engineering Lead

Co-Founder / CTO Opportunity – AI Tech Recruitment Start-Up

Co-Founder / CTO Opportunity – AI Tech Recruitment Start-Up

Principal Software Engineer

Head of Software Engineering | £150k – Java, Machine Learning, and Data-Driven Innovation

Technical Lead Software Engineer (JN -112024-403307) Bristol City Centre, Bristol, England

Technical Lead Full Stack Software Engineer

Join a pioneering organisation that is transforming the cyber reinsurance industry through advanced analytics and innovative technology! We’re on a mission to enhance how cyber risk is assessed and managed, and we want you to be a part of this exciting journey.

About the Role:

We are on the lookout for a dynamic and experienced Technical Lead Full Stack Software Engineer to join our team in Bristol. If you have a passion for building data-intensive applications and a knack for leadership, this role is designed for you!

Key Responsibilities:

  • Architect and Develop:Lead the design and development of our cutting-edge cyber reinsurance platform, focusing on features like:
    • Ingestion of reinsurance submissions
    • Policy administration
  • Lead and Inspire:Manage and grow a talented full-stack engineering team, specialising in high-performance computing and large-scale data engineering.
  • Collaborate Across Teams:Work closely with data science and modelling teams to ensure seamless integration of analytical models into our platform.
  • Strategic Scaling:Develop strategies to scale our platform across additional lines of business.
  • Hands-On Contribution:Dive into the codebase, tackle technical challenges, and mentor your team members.

What You Bring:

  • Experience:10+ years in software engineering with a focus on data-intensive applications, particularly in the financial sector. A minimum of 5 years in a leadership role is essential.
  • Technical Expertise:

- Strong full-stack development skills, with hands-on experience in front-end web applications.
- Proficiency in Python and a deep understanding of machine learning and analytics.
- Experience with cloud infrastructure (GCP, AWS, Azure), DevOps practices, Docker, Terraform, Kubernetes, and data lakehouses like Databricks.
- Familiarity with CI/CD pipelines and automated testing frameworks.

  • Leadership Skills:Proven ability to build and lead high-performing teams, fostering collaboration between engineering and data science.
  • Hands-On Aptitude:A willingness to contribute directly to coding and problem-solving.

Skills and Attributes:

  • Strategic Thinker:Align engineering initiatives with business goals.
  • Excellent Communicator:Convey technical concepts clearly to diverse stakeholders.
  • Innovative Mindset:Stay updated with emerging technologies and industry trends.
  • Problem Solver:Tackle complex challenges with strong analytical skills.
  • Team Player:Foster a collaborative and inclusive environment.

Why Join Us?

This is not just another job; it’s an opportunity to lead a talented team while driving innovation in a rapidly evolving industry. If you're ready to take the next step in your career and make a real impact, we want to hear from you!

Apply Today!Your next big adventure awaits!

Adecco is a disability-confident employer. It is important to us that we run an inclusive and accessible recruitment process to support candidates of all backgrounds and all abilities to apply. Adecco is committed to building a supportive environment for you to explore the next steps in your career. If you require reasonable adjustments at any stage, please let us know and we will be happy to support you.

#J-18808-Ljbffr

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.