Senior Machine Learning Engineer (AWS)

Location: Remote from Spain; candidates must be based in Spain and already have the right to work in Spain. Visa sponsorship and relocation support are not available.

Salary: €70,000–€80,000 per annum, depending on experience.

Company

hirify.global delivers customizable, mission-critical communication solutions for remote sites worldwide and operates a data lakehouse platform for analytics across the business.

What you will do

• Design, build, evaluate, and deploy forecasting, anomaly detection, and classification models using network telemetry and business data.
• Build and maintain feature and training-data pipelines from streaming and batch sources on the data lakehouse.
• Own MLOps practices including experiment tracking, model versioning and registries, model CI/CD, automated retraining, and drift monitoring.
• Define standards for model development, evaluation, documentation, and governance.
• Translate business needs into ML problems and explain model behavior, limitations, and business impact to technical and non-technical stakeholders.
• Collaborate with data engineers to deploy reliable, observable ML services on AWS, mentor AI Engineers and data scientists, and support Agile delivery.

Requirements

• 5+ years of experience in machine learning, data science, or ML engineering, including delivering and operating production models.
• Strong Python programming and deep familiarity with the ML ecosystem, including scikit-learn, XGBoost, PyTorch or TensorFlow, and pandas.
• Strong SQL skills and experience with large analytical datasets.
• Hands-on experience with time-series modeling, including forecasting and/or anomaly detection at scale.
• Solid knowledge of statistics, experiment design, and model evaluation methodology.
• Hands-on AWS experience, including services such as SageMaker, Lambda, Step Functions, S3, and ECR, and experience deploying cloud ML workloads.
• Experience with Docker, MLOps tooling and practices, Git, and Agile/Scrum environments.
• Excellent communication and stakeholder management skills, including explaining model behavior to non-technical audiences.

Nice to have

• Experience with streaming ML data, Kafka/Confluent, Flink, or near-real-time inference.
• Experience with lakehouse architectures, Apache Iceberg, Parquet, medallion/dbt patterns, Dremio, or TimescaleDB.
• Exposure to LLMs and generative AI systems, including evaluation of generative outputs.
• Experience in telecommunications, satellite, networking, or IoT telemetry.
• Infrastructure as Code and CI/CD experience, including CloudFormation or Terraform.
• Advanced AWS expertise, relevant AWS or ML certifications, mentoring experience, or experience leading technical workstreams.

Culture & Benefits

• Work with a global team and clients across all continents on communications projects undergoing significant transformation.
• Access opportunities to develop skills across departments and gain new responsibilities through internal promotion.
• Work on diverse projects across customer industries and choose between traditional desk-based or field environments where applicable.
• Collaborate in a small, agile team with an emphasis on experimentation, learning, technical quality, and impact.