At a Glance
- Tasks: Develop and deploy AI solutions using cutting-edge machine learning technologies.
- Company: Join a forward-thinking tech team in Erskine, Scotland.
- Benefits: Competitive pay, flexible work, health perks, and continuous learning opportunities.
- Other info: Collaborative culture with excellent career growth and fun team events.
- Why this job: Make a real impact on innovative AI projects that shape the future.
- Qualifications: Experience in MLOps, strong Python skills, and familiarity with ML lifecycle tools.
The predicted salary is between 50000 - 60000 £ per year.
Location: Erskine, Scotland, Hybrid
Security Clearance level: SC
Eligibility: Candidates must be UK national/sole British citizens and resided in the UK for 5 years or over.
Key Responsibilities:
- Strong proficiency in Python and ML libraries such as pandas, NumPy, scikit-learn, XGBoost, LightGBM, CatBoost, TensorFlow, Keras, PyTorch.
- Experience with model deployment and serving tools: ONNX, TensorRT, TensorFlow Serving, TorchServe.
- Familiarity with ML lifecycle tools: MLflow, Kubeflow, Azure ML Pipelines.
- Experience working with distributed data processing using PySpark.
- Solid understanding of software engineering principles and version control (e.g., Git).
- Excellent problem-solving skills and ability to work independently or in a team.
- Collaborate with cross-functional teams to integrate AI solutions into scalable products.
- Ensure best practices in data engineering and contribute to architectural decisions.
- Support senior team members in identifying and addressing data science opportunities.
Required Skills & Experience:
- Proven experience in MLOps or DevOps roles within machine learning environments.
- Strong programming skills in Python, with hands-on experience in PySpark and SQL.
- Deep understanding of ML lifecycle management and CI/CD best practices.
- Familiarity with cloud-native ML platforms and scalable deployment strategies.
- Demonstrated relevant industry experience, including time spent in a similar role.
- Proficiencies in data cleansing, exploratory data analysis, and data visualization.
- Continuous learner that stays abreast with industry knowledge and technology.
Why Join Us?
- Work on impactful AI projects with real-world applications.
- Be part of a collaborative and forward-thinking team.
- Access to continuous learning and development opportunities.
- Flexible working arrangements and a supportive work culture.
Benefits:
- Competitive compensation.
- Pension scheme.
- DXC Select – Our comprehensive benefits package (includes private health/medical insurance, gym membership and more).
- Perks at Work (discounts on technology, groceries, travel and more).
- DXC incentives (recognition tools, employee lunches, regular social events etc).
Ready to shape the future of AI? Apply now and bring your expertise to a team that values innovation, creativity, and excellence.
Data/Machine Learning Ops Engineer in Erskine employer: 慨正橡扯
Join a dynamic team in Erskine, Scotland, where you will work on impactful AI projects that drive real-world applications. Our collaborative and forward-thinking culture fosters continuous learning and development, offering flexible working arrangements and a comprehensive benefits package that includes private health insurance and discounts on various services. As a Data/Machine Learning Ops Engineer, you'll have the opportunity to grow your skills while contributing to innovative solutions in a supportive environment.
StudySmarter Expert Advice🤫
We think this is how you could land Data/Machine Learning Ops Engineer in Erskine
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at local meetups. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving Python and ML libraries. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on common MLOps questions and practical scenarios. Practice explaining your thought process and problem-solving approach, as this is key in demonstrating your expertise.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are genuinely interested in joining our team.
We think you need these skills to ace Data/Machine Learning Ops Engineer in Erskine
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your experience with Python and ML libraries like pandas and TensorFlow. We want to see how your skills match the job description, so don’t be shy about showcasing relevant projects!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Tell us why you’re passionate about MLOps and how your background makes you a great fit for our team. Keep it engaging and personal – we love to see your personality come through.
Showcase Your Problem-Solving Skills:In your application, give examples of how you've tackled challenges in previous roles. We value excellent problem-solving skills, so share specific instances where you’ve made an impact or improved processes.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, you’ll find all the details you need about the role and our company culture there!
How to prepare for a job interview at 慨正橡扯
✨Know Your Tech Stack
Make sure you brush up on your Python skills and the ML libraries mentioned in the job description. Be ready to discuss your experience with tools like TensorFlow, PyTorch, and ONNX. Having specific examples of projects where you've used these technologies will really impress the interviewers.
✨Showcase Your Problem-Solving Skills
Prepare to talk about challenges you've faced in previous roles and how you tackled them. Use the STAR method (Situation, Task, Action, Result) to structure your answers. This will demonstrate your analytical thinking and ability to work independently or as part of a team.
✨Familiarise Yourself with MLOps Practices
Since the role focuses on MLOps, make sure you understand the ML lifecycle management and CI/CD best practices. Be ready to discuss how you've implemented these in past projects, especially if you've worked with tools like MLflow or Azure ML Pipelines.
✨Ask Insightful Questions
Interviews are a two-way street! Prepare thoughtful questions about the team's current projects, the company culture, and opportunities for continuous learning. This shows your genuine interest in the role and helps you assess if it's the right fit for you.
