Senior Fintech Machine Learning Platform Engineer

Build the Infrastructure That Turns Financial AI Into Production Systems

An innovative fintech organization in Estonia is seeking a Senior Fintech Machine Learning Platform Engineer to build the infrastructure required to develop, deploy, monitor, and scale machine-learning systems across financial products and operations.

This role is focused on the engineering layer between data science and production. You will create reliable platforms that allow data scientists and AI engineers to move models from experimentation into secure, observable, high-performance financial applications.

Key Responsibilities

• Design and operate scalable machine-learning infrastructure for fintech applications.
• Build reusable platforms for model training, validation, deployment, serving, and monitoring.
• Develop automated MLOps pipelines supporting continuous model experimentation and production delivery.
• Integrate machine-learning systems with transaction, customer, financial, and operational data platforms.
• Establish reliable mechanisms for feature management, model versioning, data validation, and model lifecycle management.
• Develop infrastructure for real-time and batch model inference.
• Implement monitoring for model performance, data drift, infrastructure health, latency, and operational reliability.
• Collaborate with data scientists and software engineers to productionize complex financial models.
• Optimize model-serving infrastructure for scalability, performance, and cost efficiency.
• Establish appropriate security and access controls around sensitive financial and customer data.
• Support experimentation involving generative AI, predictive analytics, anomaly detection, and intelligent automation.
• Establish engineering standards for reproducibility, testing, deployment, and model governance.
• Mentor engineers and contribute to the organization’s long-term AI platform strategy.

What You Bring

• 5+ years of experience in machine-learning engineering, platform engineering, software engineering, or MLOps.
• Strong Python and software-engineering skills.
• Experience building and operating production machine-learning systems.
• Strong understanding of ML pipelines, model serving, feature engineering, model monitoring, and data validation.
• Experience with Kubernetes, Docker, cloud platforms, and infrastructure-as-code.
• Strong understanding of APIs, distributed systems, databases, and event-driven architectures.
• Experience with technologies such as MLflow, Spark, Kafka, Airflow, or comparable platforms.
• Experience working with large-scale or sensitive financial datasets is highly valuable.
• Strong understanding of security, access management, and data governance.
• Excellent analytical and troubleshooting capabilities.

Technical Environment

The platform may include Python, Kubernetes, Docker, MLflow, Spark, Kafka, Airflow, PostgreSQL, AWS/Azure/GCP, feature stores, model-serving platforms, Terraform, and automated MLOps pipelines.

Why This Opportunity

Financial AI is only valuable when it can operate reliably in production. This position gives you the opportunity to build the infrastructure that enables advanced machine learning to support financial intelligence, automation, customer experiences, risk analysis, and operational decision-making at scale.

Ideal Candidate

You are an engineer who understands that production AI requires far more than a good model. You care about reproducibility, reliability, observability, data quality, security, latency, scalability, and operational governance, and you enjoy creating platforms that allow AI teams to deliver real financial products.

Are you interested in this position?

Apply by clicking on the “Apply Now” Button below!

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