AI Software Engineer
Role details
Job location
Tech stack
Job description
As an AI Software Engineer, you'll help build the next generation of intelligent observability systems - combining backend engineering, data analytics, and applied AI to improve reliability, automation, and insight generation across LiveRamp's large-scale data pipelines. You'll collaborate with backend engineers, data scientists, and reliability leads to design and ship production-ready AI components that detect, explain, and even self-heal anomalies in distributed systems., * Develop backend services and APIs integrating AI/ML components for intelligent monitoring and automated diagnostics.
- Build data pipelines for training, evaluation, and inference of anomaly-detection and root-cause-prediction models.
- Implement statistical and ML techniques to analyze metrics, logs, and traces - enabling proactive incident detection.
- Collaborate with engineers and analysts to translate data patterns into actionable system insights and reliability improvements.
- Contribute to internal dashboards or visualization tools that surface model predictions and performance metrics.
- Maintain CI/CD pipelines, testing suites, and lightweight model deployment workflows (Docker, MLflow, etc.).
- Continuously learn and apply the latest AI/ML and observability tools to production-scale systems.
Requirements
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1-2 years of experience in software development with exposure to AI/ML applications or data-driven systems.
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Proficiency in Python and familiarity with one or more of: Java, Go, or TypeScript.
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Experience using ML frameworks such as PyTorch, scikit-learn, or TensorFlow.
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Working knowledge of SQL and experience with large datasets (Spark, Snowflake, or similar).
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Familiarity with REST/gRPC API design, Docker, and Git workflows.
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Curious mindset - able to bridge the gap between software reliability and applied machine learning. Bachelor's degree in Computer Science, Software Engineering, Data Science, or related technical field., * Experience with Observability platforms (Grafana, Prometheus, OpenTelemetry) or time-series data analysis.
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Exposure to MLOps pipelines (Airflow, MLflow, Kubeflow) and production inference scaling.
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Understanding of distributed data systems (Kafka, Spark, etc.).
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Prior experience building experimental prototypes or research-driven AI features.
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Multilingual or international experience - our team collaborates across U.S., EMEA, and APAC regions.