Machine Learning Engineer

Signicat is a pioneering Pan-European Digital Identity Service Provider, supporting over 170 data sources for seamless identity verification. Trusted by governments, financial institutions, and businesses worldwide. Such as General Financing, Compensa Life, Telia, Rimi, Volvo, and many more.

Signicats are present all over Europe, with 13 offices in major cities across the continent. Our team of 500+ professionals are driven by an impactful mission - to build technology for people to trust each other in a digital world.

The Role

We are looking for an ML / AI Specialist to join our Computer Vision team in Madrid. You’ll focus on building scalable, secure, and production-ready ML systems, with a strong emphasis on MLOps, data engineering, and governance—rather than traditional data science.

Working on digital identity solutions like document verification and biometric authentication, you’ll collaborate closely with engineering, product, and security teams to deliver reliable AI services across Signicat’s platform in regulated environments.

Key Responsibilities

• Design and maintain data pipelines for structured and unstructured data using tools such as Kafka, Spark, Beam, or Flink.
• Implement data quality, lineage, and governance to ensure GDPR compliance and auditability.
• Create and manage datasets for model training using automated or semi-automated data curation techniques.
• Develop and maintain CI/CD workflows for model packaging, testing, and deployment
• Monitor models in production and implement processes for auto-retraining and performance alerts.
• Collaborate with backend engineers to expose models through secure APIs.
• Advocate and contribute to best practices for secure, scalable, and compliant AI/ML systems.
  

Desired Qualifications

• 3–5 years of professional experience in machine learning, MLOps, or data engineering.
• Hands-on experience with processing multimodal and streaming data, including images, video, logs, and structured data.
• Strong knowledge of big data tools (Spark, Beam/Flink, Kafka) and cloud storage (S3, GCS).
• Familiarity with CI/CD tools and infrastructure for ML (e.g., GitHub Actions, MLflow, Kubeflow).
• Experience deploying models to Kubernetes or serverless environments on AWS or GCP
• Background in biometric systems, computer vision, or model governance is a strong plus.
• Understanding of security practices and compliance requirements in ML systems.
  
  

Our Tech Stack

• Languages: Python, Java (Spring)
• Data Tools: Apache Spark, Beam, Flink, Kafka, dbt
• AI/ML Frameworks: TensorFlow, PyTorch, OpenCV, ONNX, MLflow, Kubeflow
• Cloud Platforms: GCP (GKE, Vertex AI), AWS (EKS, SageMaker)
• Infrastructure: Docker, Kubernetes, Helm, Terraform
• Monitoring: Prometheus, Grafana, OpenTelemetry, Power BI
• APIs: REST and gRPC
• OS: Linux
  
  

What we offer:

• Your tech, your way - choose the tech gear that aligns with your comfort and meets your preferences.
• Hybrid work style - collaborate with the team in-office several days a week and embrace the flexible work model.
• Work-life balance - enjoy an extra 5 paid days off annually, and we will celebrate your work anniversaries with additional days off to recognize and reward your milestones.
• Modern workplace - a cozy office in Madrid with just a 15-minute walk from Atocha central station, surrounded by green recreational areas and parking available.
• Continuous growth - quarterly development talks, a dedicated time during working hours each week for training and development, unlimited access to Udemy courses, defined career paths and internal mentorship opportunities.
• Customized onboarding - we will prepare an Onboarding Plan to ensure an easy and smooth beginning.
• Be part of an international team - the opportunity to work from any other Signicat office for 2 months per year and 3 weeks per year from any European country.
• Give back to the community - option to use 16 hours of paid volunteer time each year to support a charity or cause you are passionate about.
• Empowering feedback culture - by using employee engagement tools, we ensure that every voice is heard, empowering us to grow together.