AI Software Engineer

TL;DR

AI Software Engineer (AI/Observability): Building intelligent observability systems by integrating applied AI into large-scale data pipelines with an accent on anomaly detection and automated diagnostics. Focus on designing production-ready AI components, implementing statistical models for incident prediction, and bridging the gap between software reliability and machine learning.

Location: Must be based in London, UK (Hybrid: 2 days in office required)

Company

hirify.global is a leading data collaboration platform focused on consumer privacy, data ethics, and foundational identity solutions for global innovators.

What you will do

• Develop backend services and APIs that integrate AI/ML components for automated system monitoring.
• Build and maintain data pipelines for training, evaluating, and deploying anomaly-detection models.
• Implement statistical and ML techniques to analyze system metrics, logs, and traces for proactive incident detection.
• Collaborate with reliability leads and data scientists to translate data patterns into actionable system improvements.
• Contribute to internal dashboards and visualization tools to surface model predictions.
• Maintain CI/CD pipelines and lightweight model deployment workflows using Docker and MLflow.

Requirements

• 1-2 years of experience in software development with exposure to AI/ML or data-driven systems.
• Proficiency in Python and familiarity with Java, Go, or TypeScript.
• Experience with ML frameworks such as PyTorch, scikit-learn, or TensorFlow.
• Working knowledge of SQL and experience handling large datasets (Spark, Snowflake).
• Familiarity with REST/gRPC API design, Docker, and Git workflows.
• Must be able to work from the London office 2 days per week.

Nice to have

• Experience with observability platforms like Grafana, Prometheus, or OpenTelemetry.
• Exposure to MLOps pipelines (Airflow, Kubeflow) and production inference scaling.
• Understanding of distributed data systems like Kafka.
• Prior experience building experimental prototypes or research-driven AI features.

Culture & Benefits

• Opportunity to work on production-scale AI systems in a global, collaborative environment.
• Commitment to diversity, inclusion, and belonging across a global team.
• Focus on continuous learning and application of the latest AI/ML observability tools.
• Supportive environment with a focus on equitable hiring practices.