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For Companies At a Glance
- Tasks: Design and manage data pipelines, deploy ML models, and monitor performance.
- Company: Join a dynamic startup focused on innovative data solutions.
- Benefits: Enjoy flexible working options and a collaborative team culture.
- Why this job: Be at the forefront of ML technology and make a real impact.
- Qualifications: Proficiency in programming, cloud platforms, and a passion for MLOps required.
- Other info: Experience in fintech is a plus!
The predicted salary is between 48000 - 72000 £ per year.
About the Role
We\’re looking for an experienced Data/MLOps Engineer with a startup mentality, who will work at the heart of a dynamic, multidisciplinary and agile team. As the more senior data engineer on the team, you\’ll spend most of your time working across data and software development teams, ensuring the data science pipeline flows seamlessly as part of the product, focusing on quality, automation and security.
Key Responsibilities
- Data pipeline design & management: build and maintain robust, scalable data pipelines for ML model training and inference. Ensure data is clean, versioned, and well-documented. Work with batch and real-time (streaming) data sources
- Model deployment and product integration: package and deploy ML models into production environments using tools like Docker, and cloud-native services (e.g., Vertex AI, MLflow); design and manage scalable model inference systems (APIs, batch jobs, or streaming) so they integrate well into the core product user journeys.
- Model monitoring & maintenance: implement monitoring for model performance (accuracy, drift, latency). Set up alerts and observability tools to track data/model health in production. Automate retraining workflows based on triggers (e.g., data drift, performance drop).
Role Summary:
- End-to-End ML workflow automation: data ingestion, preprocessing, model training, validation, deployment, and monitoring; ensure reproducibility and consistency across environments (dev, demo, prod).
- Robust Data Engineering: design and build high-quality data pipelines that feed ML models. Manage feature engineering, feature stores, and real-time data transformation.
- Governance & Compliance: track and version data, models, and experiments . Ensure auditability, compliance, and reproducibility of ML workflows.
- Collaboration across product roles: work closely with: data Scientists to productionise models; Software engineers to integrate product features and manage infrastructure. Product and Analytics teams to understand data and performance needs.
We’d love to hear from you, if you have…
- Demonstrable understanding of best practices in software engineering
- Proficiency in at least one general purpose programming language (Typescript/Python) with willingness to learn new languages and technologies
- Working productive experience with Linux environment and Docker
- Experience running production systems on the cloud infrastructure/platforms (AWS/Azure/GCP) – GCP experience is a plus
- Passion for MLOps & Machine Learning Infrastructure tooling (e.g. MLFlow) that you’d like to see implemented at Good With
- Enjoy participating in the full lifecycle of the software product: from idea and design, via implementation and user interface, to operational considerations
- Be able to write clean code, take pride in your work and value simplicity, testing and productivity as part of your daily routine, always putting user experience first
- Fintech/Financial Services experience is a bonus
Senior Data/Mlops Engineer employer: Tech1M
Contact Detail:
Tech1M Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Data/Mlops Engineer
✨Tip Number 1
Familiarise yourself with the latest tools and technologies in MLOps, especially those mentioned in the job description like Docker and MLFlow. Being able to discuss your hands-on experience with these tools during interviews will show that you're ready to hit the ground running.
✨Tip Number 2
Network with professionals in the data engineering and MLOps fields. Attend meetups or webinars where you can connect with current employees at StudySmarter or similar companies. This can provide valuable insights into the company culture and expectations.
✨Tip Number 3
Prepare to discuss specific projects where you've designed and managed data pipelines. Be ready to explain the challenges you faced and how you overcame them, as this will demonstrate your problem-solving skills and experience in real-world applications.
✨Tip Number 4
Showcase your passion for MLOps by staying updated on industry trends and best practices. Consider writing a blog or sharing insights on platforms like LinkedIn about your experiences and thoughts on MLOps, which can help you stand out as a knowledgeable candidate.
We think you need these skills to ace Senior Data/Mlops Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in data engineering and MLOps. Focus on specific projects where you've designed data pipelines, deployed ML models, or worked with cloud platforms like GCP, AWS, or Azure.
Craft a Compelling Cover Letter: In your cover letter, express your passion for MLOps and machine learning infrastructure. Mention specific tools you’ve used, such as Docker or MLFlow, and how they relate to the role. Show enthusiasm for working in a dynamic, agile team.
Showcase Your Technical Skills: Include a section in your application that lists your technical skills, particularly in programming languages like Python or Typescript. Highlight your experience with Linux environments and any relevant cloud infrastructure work.
Demonstrate Collaboration Experience: Provide examples of how you've collaborated with data scientists, software engineers, and product teams in previous roles. This will show your ability to work across disciplines, which is crucial for this position.
How to prepare for a job interview at Tech1M
✨Showcase Your Technical Skills
Be prepared to discuss your proficiency in programming languages like Python or Typescript. Highlight any relevant projects where you've built data pipelines or deployed ML models, especially using tools like Docker or cloud services.
✨Demonstrate Your Understanding of MLOps
Familiarise yourself with MLOps best practices and be ready to explain how you would implement them in a production environment. Discuss your experience with model monitoring, automation, and ensuring data quality throughout the ML lifecycle.
✨Emphasise Collaboration Skills
Since the role involves working closely with data scientists and software engineers, share examples of how you've successfully collaborated in multidisciplinary teams. Highlight your communication skills and ability to understand different perspectives.
✨Prepare for Scenario-Based Questions
Expect questions that assess your problem-solving abilities in real-world scenarios. Think about challenges you've faced in previous roles, particularly around data pipeline management or model deployment, and how you overcame them.
