At a Glance
- Tasks: Deploy and scale machine learning models while collaborating with a dynamic team.
- Company: DXC Technology, committed to diversity and innovation in AI.
- Benefits: Inclusive culture, continuous learning, and opportunities for career growth.
- Other info: Supportive environment encouraging applications from all backgrounds.
- Why this job: Join a cutting-edge AI community and shape the future of technology.
- Qualifications: Strong Python skills and familiarity with ML libraries and frameworks.
The predicted salary is between 50000 - 70000 € per year.
DXC Technology is committed to building diverse, inclusive teams. We welcome applications from all backgrounds and particularly encourage interest from women, underrepresented groups, and neurodivergent candidates. We offer reasonable adjustments throughout the hiring process and are dedicated to creating a supportive, accessible environment for everyone.
Location: Erskine, Newcastle, Farnborough or London. Candidates must be eligible for clearance.
Are you passionate about bringing machine learning solutions into real-world production environments? Do you enjoy collaborating with others to build scalable, reliable systems? We are looking for a Machine Learning Ops Engineer to join our growing team. This role is ideal for someone who enjoys solving complex problems, working cross-functionally, and continuously developing their technical expertise in a supportive environment. If you don’t meet every single requirement listed below, we still encourage you to apply. We value potential, curiosity, and a willingness to learn.
Key Responsibilities- Deploying, monitoring, and scaling machine learning models in production.
- Collaborating with data scientists, engineers, and stakeholders to integrate AI solutions into scalable products.
- Supporting the full ML lifecycle, from experimentation to deployment and optimisation.
- Applying best practices in data engineering and contributing to architectural decisions.
- Using modern MLOps tools and CI/CD approaches to improve reliability and efficiency.
- Contributing to a culture of knowledge-sharing and continuous improvement.
- Strong Python skills and familiarity with ML libraries such as Pandas, NumPy, and scikit-learn.
- Experience with frameworks such as TensorFlow, Keras, or PyTorch.
- Exposure to gradient boosting tools such as XGBoost, LightGBM, or CatBoost.
- Experience with model deployment tools (e.g., ONNX, TensorRT, TensorFlow Serving, TorchServe).
- Familiarity with ML lifecycle tools such as MLflow, Kubeflow, or Azure ML Pipelines.
- Experience working with distributed data processing (e.g., PySpark) and SQL.
- Understanding of software engineering best practices, including version control (Git).
- Knowledge of CI/CD principles in ML environments.
- Experience with cloud-native ML platforms is advantageous.
- Be part of a cutting-edge AI community driving digital transformation across the UK&I.
- Work at the intersection of business innovation and emerging AI technologies.
- Opportunity to shape the future of AI adoption within some of the UK’s most impactful industries.
- Collaborative, forward-thinking environment with continuous learning and development opportunities.
Machine Learning Ops Engineer employer: DXC
DXC Technology is an excellent employer for those passionate about machine learning and AI, offering a collaborative and inclusive work culture that values diversity and continuous learning. With locations in Erskine, Newcastle, Farnborough, and London, employees benefit from a supportive environment that encourages professional growth and the opportunity to work on cutting-edge projects that drive digital transformation across various industries. Join us to be part of a forward-thinking team where your contributions can shape the future of AI adoption.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Ops Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at local meetups. We all know that sometimes it’s not just what you know, but who you know that can help you land that Machine Learning Ops Engineer role.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects. We recommend using GitHub to share your code and demonstrate your expertise with tools like TensorFlow or PyTorch. It’s a great way to stand out!
✨Tip Number 3
Prepare for those interviews! Brush up on common ML concepts and be ready to discuss your experience with deployment tools. We suggest practicing with friends or using mock interview platforms to build your confidence.
✨Tip Number 4
Don’t hesitate to apply through our website! Even if you don’t tick every box in the job description, we value potential and curiosity. So go ahead, submit your application and let us see what you’ve got!
We think you need these skills to ace Machine Learning Ops Engineer
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that align with the Machine Learning Ops Engineer role. Highlight your Python expertise and any relevant ML frameworks you've worked with, as this will catch our eye!
Craft a Compelling Cover Letter:Use your cover letter to tell us why you're passionate about machine learning and how you can contribute to our team. Share specific examples of your past projects or collaborations that showcase your problem-solving skills.
Showcase Your Continuous Learning:We love candidates who are eager to learn! Mention any courses, certifications, or personal projects related to MLOps or AI that demonstrate your commitment to staying updated in this fast-paced field.
Apply Through Our Website:For the best chance of getting noticed, apply directly through our website. It’s super easy and ensures your application goes straight to the right people. Plus, we can’t wait to see what you bring to the table!
How to prepare for a job interview at DXC
✨Know Your Tech
Make sure you brush up on your Python skills and get familiar with the ML libraries mentioned in the job description, like Pandas and NumPy. Be ready to discuss your experience with frameworks such as TensorFlow or PyTorch, as well as any model deployment tools you've used.
✨Show Your Problem-Solving Skills
Prepare to share specific examples of how you've tackled complex problems in previous roles. Think about times when you collaborated with data scientists or engineers to integrate AI solutions, and be ready to explain your thought process.
✨Understand the ML Lifecycle
Familiarise yourself with the full machine learning lifecycle, from experimentation to deployment. Be prepared to discuss best practices in data engineering and how you've contributed to architectural decisions in past projects.
✨Emphasise Collaboration
Since this role involves working cross-functionally, highlight your teamwork experiences. Share how you've contributed to a culture of knowledge-sharing and continuous improvement, and be open to discussing how you can bring that mindset to their team.
