Senior DevSecOps Engineer

Leonardo
London
1 year ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Scientist-Senior Manager

Senior Data Engineer

Data Engineer

Data Engineer

Senior Data Scientist

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.

Careers Jobs

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

Jobs

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Education Jobs

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.

🍪 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.