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- Tasks: Support a major consumer goods firm in building and maintaining ML infrastructure.
- Company: Join a leading firm driving data and AI evolution for enterprise customers.
- Benefits: Work from home with competitive pay and flexible hours.
- Why this job: Be part of an innovative team, enhancing AI solutions and making a real impact.
- Qualifications: Experience in deploying ML models, particularly in Azure Databricks and Python.
- Other info: Immediate start, 2-month contract, with occasional travel to Middlesex.
The predicted salary is between 66000 - 77000 £ per year.
An opportunity for an ML Ops Engineer to support a major consumer goods firm as they embark on the next phase of their Data and AI journey. The successful candidate will have demonstrable experience building and maintaining a robust machine learning infrastructure, supporting the successful deployment and integration of additional AI solutions.
Responsibilities:
- Identify and implement the best approach and practices to MLOps pipelines based on the clients' needs.
- Involved in gathering requirements, model registration, performing changes to APIs and models, performing re-deployment tests, handling deploying and making post-deployment checks and maintenance.
- Collaborate with Data Scientists and Platform Engineers to roll out new features and make performance improvements.
- Work with infrastructure as code technology to deploy, manage and run world-class MLOps and data solutions.
- Maintain a strong understanding of Databricks and Azure ML.
- Proactively suggest and implement changes to ways of working within and beyond the team to improve platform stability and long-term efficiency.
- Ensure smooth day-to-day running of ML Ops, driving optimization.
- Share knowledge with the wider data community.
Skills / Attributes / Experience Profile:
- Proven experience deploying and managing ML models in production, in Azure Databricks.
- Tech stack: Databricks, Unity Catalog, Python, Git, MLFlow, Delta tables, Azure DevOps.
- Hands-on development experience using Python, particularly with TensorFlow, PyTorch, scikit-learn, boto3, and the Python Data Science stack (pandas, numpy, etc.).
- Strong analytical and problem-solving skills, with the ability to work with large-scale, complex datasets.
- Experience in automation, CI/CD, and Agile development environments.
- Implementing monitoring for data drift, model drift, and performance.
- Experience of enabling Feature stores for reusability.
- Experience of event-driven and/or trigger-based applications (real-time).
- Experience of upgrading architectures from legacy versions to current versions (i.e. Core Library 2.0 to 3.0, integration of Unity Catalog, etc.).
- Ability to work independently and within a team.
- Ideally experience working in a consulting/IT Services environment.
Additional Information:
- Location: Home Based, with occasional travel to Middlesex.
- Rate Offered: £500-550 per day.
- IR35 Status: Outside.
- Start Date: Immediate / ASAP.
- Initial Duration: 2 Months.
Our Client is enabling enterprise customers to take control of their data & AI evolution by combining specialist professional services with talent placement and development for high impact solutions that endure.
MLOps Engineer employer: Shareforce
Contact Detail:
Shareforce Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land MLOps Engineer
✨Tip Number 1
Familiarise yourself with the specific tools and technologies mentioned in the job description, such as Azure Databricks, MLFlow, and Python libraries like TensorFlow and PyTorch. Having hands-on experience or projects showcasing these skills can set you apart during discussions.
✨Tip Number 2
Network with professionals in the MLOps field, especially those who have worked with consumer goods firms. Engaging in relevant online communities or attending industry meetups can provide insights and potentially lead to referrals.
✨Tip Number 3
Prepare to discuss your previous experiences with deploying and managing ML models in production. Be ready to share specific examples of challenges you faced and how you overcame them, particularly in relation to automation and CI/CD processes.
✨Tip Number 4
Showcase your problem-solving skills by preparing to discuss how you've handled data drift and model performance monitoring in past roles. Highlighting your proactive approach to optimising MLOps will resonate well with the hiring team.
We think you need these skills to ace MLOps Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with MLOps, particularly in deploying and managing ML models in Azure Databricks. Include specific projects or achievements that demonstrate your skills with the tech stack mentioned, such as Python, TensorFlow, and Azure DevOps.
Craft a Compelling Cover Letter: In your cover letter, express your enthusiasm for the role and the company. Discuss how your background aligns with the responsibilities outlined in the job description, especially your experience with MLOps pipelines and collaboration with Data Scientists and Platform Engineers.
Showcase Relevant Projects: If you have worked on relevant projects, consider including a portfolio or a brief summary of these projects in your application. Highlight any experience with automation, CI/CD, and real-time applications, as these are key aspects of the role.
Proofread Your Application: Before submitting your application, take the time to proofread it for any spelling or grammatical errors. A well-presented application reflects your attention to detail, which is crucial for an MLOps Engineer.
How to prepare for a job interview at Shareforce
✨Showcase Your MLOps Experience
Be prepared to discuss your previous experience with deploying and managing ML models, particularly in Azure Databricks. Highlight specific projects where you implemented MLOps pipelines and the impact they had on the organisation.
✨Demonstrate Technical Proficiency
Familiarise yourself with the tech stack mentioned in the job description, including Databricks, Python, and CI/CD practices. Be ready to answer technical questions or even solve problems on the spot to showcase your hands-on development skills.
✨Collaboration is Key
Emphasise your ability to work collaboratively with Data Scientists and Platform Engineers. Share examples of how you've successfully rolled out new features or improved performance through teamwork, as this role requires strong collaboration.
✨Proactive Problem-Solving
Prepare to discuss how you've proactively suggested and implemented changes in past roles to improve platform stability and efficiency. This shows your initiative and commitment to continuous improvement, which is crucial for this position.
