Senior DevSecOps Engineer

Leonardo
London
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer

Data Engineer

Data Scientist-Senior Manager

Senior Manager, Head of Data Engineering

Senior Manager, Head of Data Engineering

Senior Data Engineer

Job Description:

The Leonardo Cyber Security Division is looking to recruit Senior DevSECOps Engineers to provide a bridge between software development, testing, infrastructure, operations and system administrators to facilitate efficient, continuous and high-quality software delivery.

This is an excellent opportunity to join one of the UK’s leading aerospace companies. To support you in this role, we offer our , having won the Best Flexible Working Policy Award, Best Flexible Benefits Strategy and Best Flexible Benefits Plan in 2023.


The processes and tooling implemented by the Software/DevSECOps Engineer team will enable source code and associated artefacts to be installed and configured across environments. The Software/DevSECOps Engineer will typically combine the skills of software coding and/or scripting and process reengineering with exemplary team working and communication skills. The Software/DevSECOps Engineer should have a good breadth of knowledge across the disciplines of software development and how software is deployed.

Projects will be from the full systems engineering lifecycle from Concept Exploration through to In-Service Support providing the opportunity to develop your experience while supporting the requirements management, system architecture and design, subsystem design and development, system integration, test and acceptance and specialist technical support.

As a DevSECOps Engineer within the Cyber Division, you will typically be working as part of a larger team including the design, development and testing of secure systems, including but not limited to, Information Management Systems, Command and Control systems, Security Monitoring of IT, IoT and CNI systems and services.


These roles will have the ability to take ownership / implement the DevSECOps side of the engineering delivery of one or more work packages, including the planning, estimation, execution and progress reporting of own workload and that of other team members.

Assist in the overall implementation of new software / applications / packing for the Cyber division. Analyse requirements, design, implement and unit test software code and supporting artefacts using appropriate tools. As required, lead on software/application development activities. Plan and undertake installations on development, test, reference and operational environments. Deliver consistent high-quality software and environmental builds through automation. Creation and maintenance of automaton frameworks for software and/or environment provisioning and ongoing operation. Managing and controlling software configuration for projects including the source repository. Implement and maintain of Continuous Integration (CI) and/or Build pipelines where appropriate. Understand change control and release management practices. Assist in data engineering activities (data cleansing, integration, onward data analytics). Provide application support to existing deployed services. Contribute to the improvement and efficiency of the Cyber division.

A good working knowledge and experience of various techniques with a broad scope of skills and experience of mainstream IT infrastructure services and components including:

Demonstrable experience of performing a similar role. Good knowledge of Agile methodologies, SCRUM, BDD, TDD. Source control management (Git, Azure DevSECOps etc). O/S - Linux, Windows level scripting. Automation tools for software and/or infrastructure builds PowerShell / Ansible etc). Clear and effective communication skills. Strong analytical skills. Aptitude for solving complex/technical problems. Flexible and adaptable attitude, capable of acquiring new skills. Objective and logical with an enquiring and creative mind Microsoft - C#, .NET, SharePoint, SQL. Data Engineering – experience of one or more: Apache ecosystem, SQL, Python. Web - HTML, CSS, JavaScript, XML, SOAP. Experience with Secure DevSECOps within an Agile /SAFe environment. Containerisation - Docker, Kubernetes, Kubeflow etc. Software development capability. Any form of prior development expertise would be beneficial, but the eagerness and desire to learn new technologies would be preferable.

Security Clearance

:

Life at Leonardo

With a company funded benefits package, a commitment to learning and development, and a flexible approach to working hours focused on the needs of both our employees and customers, a career with Leonardo has never offered as many opportunities or been more accessible to as many people.

Pension:Award winning pension scheme (up to 10% employer contribution)Holidays:25 days plus bank holidays, option to buy/sell leave and to accrue up to 12 additional flexi leave days per yearFlexible Working:Flexible hours with hybrid working options. For part time opportunities, please talk to usWellbeing: Employee Assistance Programme, access to Mental Health support, Financial wellbeing support, network groups (Enable, Pride, Equalise, Reservists, Carers)Lifestyle:Discounted Gym membership, Cycle to work schemeCompany funded flexible benefits:Access to private healthcare, dental schemes, Workplace ISA, Go Green Car Scheme, technology and lifestyle options (£500 annual allowance)Training:Free access to more than 4000 online courses via CourseraReferral Incentive:You can earn a reward for successfully referring a friend or family memberBonus:Scheme in place for all employees at management level and below

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

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

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

Careers Jobs

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

Education

The Skills Gap in Machine Learning Jobs: What Universities Aren’t Teaching

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.

🍪 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.