At a Glance
- Tasks: Develop and implement AI/ML solutions for the British Army's training objectives.
- Company: Join OMNIA Training, a leader in innovative defence training.
- Benefits: Competitive salary, professional development, and mentorship opportunities.
- Other info: Collaborative environment with a mission-focused team and excellent career growth.
- Why this job: Shape the future of military training with cutting-edge AI technology.
- Qualifications: Strong Python skills and experience with major ML frameworks required.
The predicted salary is between 70100 - 100000 £ per year.
hackajob is collaborating with Raytheon to connect them with exceptional professionals for this role. Are you the right applicant for this opportunity? Find out by reading through the role overview below.
About us: At OMNIA Training, we’ve brought together some of the UK’s most innovative defence training organisations under one powerful mission: to transform the British Army’s training system and create the best-trained Army in the world. OMNIA are redefining the British Army’s collective training. To do that, we are looking for the best and brightest minds from across the UK. We are backed by British innovation and powered by world-class experts, like you. OMNIA is at the heart of the UK’s bold Land Industrial Strategy.
The role: You’ll work in a matrix organisation and report operationally through OMNIA Training and functionally through the OMNIA AI Solutions Lead. Ultimately, you’ll work for the British Army, championing innovation, and helping shape the future of military collective training.
Key Responsibilities:
- Support the development and implementation of AI/ML solutions ensuring alignment with Army training objectives and Omnia architecture principles.
- Contribute to the design, development, and deployment of AI/ML solutions aligned to Army training objectives and enterprise architecture principles.
- Develop and maintain ML pipelines including data preprocessing, feature engineering, model training, validation, and performance evaluation.
- Support integration of AI capabilities into secure operational environments, ensuring compatibility with DevSecOps pipelines and platform constraints.
- Monitor deployed models, analyse performance metrics, and implement tuning or retraining strategies to maintain operational effectiveness.
- Collaborate with data engineers and software teams to ensure scalable, maintainable, and secure AI components.
- Apply ethical AI principles including bias mitigation, data governance, and regulatory compliance in defence contexts.
- Produce high-quality technical documentation to support maintainability, auditability, and knowledge transfer.
- Support stakeholder engagement through demonstrations, technical briefings, and translation of complex AI concepts into accessible insights.
- Evaluate emerging AI tools and platforms for applicability to Army training use cases.
- Actively develop technical capability through structured learning and mentorship.
- All other tasks required to deliver the programme.
Who we are looking for:
You’ll have a mission focus, and the enthusiasm and drive to get things done. You’ll want to work in collaboration with other defence training organisations, and the British Army. You won’t let bureaucracy get in the way of what needs to be done, you’ll learn lessons and share these lessons across the team. You’ll understand what it means to put the mission first.
The successful candidate will support the development and integration of AI/ML models aligned with Army training goals and OMNIA architecture, assisting with data preparation, model evaluation, and workflow automation. They will collaborate with technical teams, document processes, monitor model performance, and contribute to ethical AI practices. The role also involves learning emerging technologies, supporting stakeholder communications, and working within DevSecOps engineers to ensure smooth deployment and continuous improvement.
Essential Skills and Experience:
- Strong Python programming skills for AI/ML development.
- Hands-on experience with at least one major ML framework (e.g. PyTorch, TensorFlow, scikit-learn).
- Experience building and maintaining end-to-end ML pipelines (data ingestion, pre-processing, training, evaluation, deployment) and deployment to production environments.
- Understanding of feature engineering, model validation, and performance evaluation techniques.
- Experience integrating ML components into wider software systems via APIs or microservices.
- Experience working with version control (Git) and collaborative engineering practices.
- Experience contributing to CI/CD pipelines and containerised deployments (e.g. Docker, Kubernetes).
- Experience working with cloud or controlled compute environments.
- Ability to document technical solutions for maintainability, audit, and operational support.
- Understanding of secure coding principles and data protection requirements.
- Awareness of responsible AI considerations (bias mitigation, explainability, privacy).
- Ability to work within regulated or governed environments and frameworks.
- Strong analytical and problem-solving capability.
- Ability to communicate technical concepts to non-technical stakeholders.
- Experience collaborating within cross-functional engineering teams.
StudySmarter Expert Advice🤫
We think this is how you could land AI Solution Engineer in Woking
✨Join Local Tech Meetups
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We think you need these skills to ace AI Solution Engineer in Woking
Some tips for your application 🫡
Show off your coding skills:When applying for a software engineering role, it's super important to showcase your coding skills. Make sure your CV includes your tech stack, any relevant programming languages you’re comfortable with, and examples of projects you've worked on. If you have a GitHub profile, link it up! We love to see code in action.
Tailor your portfolio:For a full-time role, we’d expect to see some solid examples of your work in your portfolio. Make sure to include at least two or three projects that highlight your problem-solving skills and your ability to work with different technologies. Focus on the projects that are most relevant to the position at Raytheon.
Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at Raytheon and how your skills align with the role. Show us your passion for software development. We dig enthusiastic candidates who understand the value of collaboration and continuous learning!
Be clear and concise:When it comes to writing your CV and cover letter, clarity is key. Avoid jargon that could confuse us and stick to simple, direct language. Highlight your achievements with quantifiable results where possible, and keep everything easy to read. A well-organised application goes a long way!
How to prepare for a job interview at Raytheon
✨Brush Up on Your Coding Skills
For a full-time software engineering role, it's crucial that we stay sharp with our coding abilities. Expect technical questions that might involve solving problems on the spot or discussing algorithms. Practise on platforms like LeetCode or HackerRank to get comfortable with the types of questions that often come up.
✨Know Your Tools and Frameworks
Make sure we’re well-acquainted with the tools and technologies listed in the job description. Familiarise ourselves with any specific frameworks or programming languages mentioned. If Raytheon uses React or Node.js, for instance, be ready to discuss how we’ve used them in previous projects or coursework.
✨Showcase Your Projects
Bring along a portfolio that highlights our best work. This could be code samples, GitHub repositories, or any side projects we’ve built. Make sure we can talk through our thought process for each project, especially the challenges we faced and how we solved them—this shows our problem-solving skills in action.
✨Prepare for Behavioural Questions
While technical skills are key, full-time positions also require cultural fit. Be ready to discuss our previous experiences and how we handle teamwork, conflict, and deadlines. Brush up on the STAR method—Situation, Task, Action, Result—to clearly articulate our past experiences when discussing how we've contributed to a team.