Dxc Technology
MachineLearningOpsEngineer
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Machine Learning Ops Engineer at Dxc Technology. Skills: Machine Learning, MLOps, Python, Cloud. Deploy models. Monitor models”
Industry & Context.
Solving complex problems
Eligible for clearance, Sole UK national
What They're Looking For.
Must Have
sole UK national
Nice to Have
cloud-native ML platforms
What You'll Do.
Integrate AI solutions
Apply data engineering practices
Contribute architectural decisions
How You'll Work.
Team & Collaboration
Collaborate with data scientists; Collaborate with engineers; Collaborate with stakeholders
Full Job Description
**Job Description:** 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. ## ## Machine Learning Ops Engineer **Location:** Erskine or Newcastle Hybrid working applies - 2/3 days in the office Candidates are required to be eligible for clearance and be a **sole UK national** ### 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. **Key Skills & Experience** * 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 Cat
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