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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 work options, a collaborative culture, and opportunities for growth.
- Why this job: Be part of an agile team making a real impact in the fintech space.
- Qualifications: Proficiency in programming, cloud platforms, and a passion for MLOps required.
- Other info: Experience in fintech is a bonus; we value clean code and user experience.
The predicted salary is between 43200 - 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 GCP. Having hands-on experience or projects showcasing these skills can set you apart during discussions.
✨Tip Number 2
Network with professionals in the data engineering and MLOps fields. Attend meetups, webinars, or online forums where you can connect with others who work in similar roles. This can lead to valuable insights and potential referrals.
✨Tip Number 3
Prepare to discuss your past experiences with data pipeline design and model deployment. Be ready to share specific examples of challenges you've faced and how you overcame them, as this will demonstrate your problem-solving skills and expertise.
✨Tip Number 4
Show your passion for MLOps by staying updated on industry trends and best practices. Consider contributing to open-source projects or writing articles about your experiences, which can showcase your commitment and knowledge in the field.
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: Clearly list your programming skills, especially in Python or Typescript, and any experience with Linux environments. Provide examples of how you've applied these skills in previous roles, particularly in building scalable data pipelines.
Highlight Collaboration Experience: Emphasise your ability to work across teams, such as collaborating with data scientists and software engineers. Share examples of how you’ve contributed to product integration and ensured smooth workflows in past projects.
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.
✨Emphasise Collaboration Experience
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 integrate feedback.
✨Prepare for Scenario-Based Questions
Expect questions that assess your problem-solving abilities in real-world scenarios. Think about challenges you've faced in data pipeline management or model deployment, and how you overcame them while ensuring compliance and governance.
