Principal DevOps/Cloud Engineer

intelmatix
London
1 month ago
Applications closed

Related Jobs

View all jobs

Principal Data Scientist

Principal Naval Architect (Weights)

Principal Naval Architect (Weights)

Principal Naval Architect (Weights)

Principal Naval Architect (Weights)

Principal Naval Architect (Weights)

Role Overview

Join our team as the Principal Cloud/DevOps Engineer, a key player in our technological evolution. This role goes beyond traditional cloud infrastructure and DevOps, encompassing the automation and deployment of advanced computational processes, including machine learning models. You will be instrumental in ensuring our cloud infrastructure is robust, scalable, secure, and efficient, with a focus on supporting diverse technological solutions.

Key Responsibilities

  1. Lead the management and optimization of our cloud environment, focusing on performance, security, and scalability.
  2. Automate infrastructure provisioning and management, integrating complex computational and analytical model deployments.
  3. Develop and enforce comprehensive security policies for both cloud infrastructure and data-intensive applications.
  4. Work closely with development teams to optimize applications, machine learning model performance and help with setting up tools ensuring efficient deployment and monitoring.
  5. Design and implement CI/CD pipelines that support both software and data-driven workflows.
  6. Conduct regular security audits, adapting to the evolving demands of a data-centric infrastructure.
  7. Champion best practices in cloud management, DevOps processes, and compliance for data-intensive applications.

Required Qualifications

  1. Bachelor’s degree in Computer Science, Information Systems, or a related field, or equivalent professional experience.
  2. Minimum of 8 years in Platform/DevOps Engineering, with experience in cloud-based data processing and deployment strategies.
  3. Expertise in managing public cloud environments (AWS preferred), including proficiency with data services and ML model deployment tools.
  4. Skilled in Infrastructure-as-Code (IaC) using tools like Terraform, and in automating data processing tasks.
  5. Experience with CI/CD tools (GitHub Actions, Jenkins, AWS CodePipeline), and integrating data-centric workflows.
  6. Familiarity with monitoring and logging tools (e.g., Prometheus, Loki, Grafana) in application and data-intensive environments.
  7. Proficiency in Configuration Management tools (Chef, Puppet, Ansible) and data orchestration tools (e.g., Airflow, Prefect).
  8. Strong background in containerization using Docker and orchestration with Kubernetes.
  9. In-depth knowledge of Linux, SQL, cloud security, scripting for automation (Python, Bash), load balancing technologies, and CDN.
  10. Agile methodology experience, excellent communication, and leadership skills.
  11. Adaptable, self-motivated, and capable of thriving in a fast-paced, unstructured startup environment.

Nice to Have

  1. AWS Certifications (AWS Certified Solutions Architect, DevOps Engineer).
  2. Extensive experience with scalable deployment of data processing and machine learning models (batch as well as real-time).
  3. Practical experience in developing and maintaining ML systems with tools such as MLflow, BentoML, and Evidently AI.
  4. Exposure to learning methodologies leveraging advanced modeling frameworks such as PyTorch and TensorFlow will be beneficial.
  5. Familiarity with data governance and compliance standards.
  6. Certification as a Kubernetes Administrator or Developer (CKA/CKAD).
  7. Exposure to diverse cloud compute and data processing tool stacks (AWS, Azure, GCP, open source).

Employee Benefits

At Intelmatix, our benefits package is designed to meet the diverse needs of our employees, reflecting our dedication to their well-being and professional growth. Depending on your office location and specific needs, our benefits may include:

  1. Comprehensive Medical Insurance for you and your dependents.
  2. In-Office Snacks Pantry.
  3. Relocation Support.
  4. Childrens School Allowance.
  5. Role-Related Training Support.
  6. Wellness Programs.
  7. Salary Advance for Housing Costs.
  8. Travel Tickets.
  9. Pension Contributions.

We are committed to continuously enhancing our benefits package to adapt to the unique needs and circumstances of our valued team members, ensuring a supportive and enriching environment for everyone at Intelmatix.

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.

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.