Senior Data/Mlops Engineer

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