Hybrid AI Software Engineer - Observability & Reliability

This will initially be a six month fixed term contract with the possibility of extension. The role requires two days in office attendance. 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.

Key Responsibilities

• 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.

Qualifications

• 1‑2 years of experience in software development with exposure to AI/ML applications or data‑driven systems.
• Proficiency in Python and familiarity with one or more of: Java, Go, or TypeScript.
• Experience using ML frameworks such as PyTorch, scikit‑learn, or TensorFlow.
• Working knowledge of SQL and experience with large datasets (Spark, Snowflake, or similar).
• Familiarity with REST/gRPC API design, Docker, and Git workflows.
• 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.

Bonus Points

• Experience with Observability platforms (Grafana, Prometheus, OpenTelemetry) or time‑series data analysis.
• Exposure to MLOps pipelines (Airflow, MLflow, Kubeflow) and production inference scaling.
• Understanding of distributed data systems (Kafka, Spark, etc.).
• Prior experience building experimental prototypes or research‑driven AI features.
• Multilingual or international experience — our team collaborates across U.S., EMEA, and APAC regions.

We use automated decision systems (ADS) as part of our recruitment and hiring process. If you require an accommodation or believe that the use of an ADS may create a barrier to your application or participation in the hiring process due to a disability or other protected characteristic, please let us know. We are committed to providing reasonable accommodations and ensuring an equitable hiring experience for all candidates. We are proud to be an equal employment opportunity and affirmative action employer. We believe in diversity and do not discriminate based on race, color, religion, sex, age, national origin, veteran status, sexual orientation, gender identity, disability, or any other basis of discrimination prohibited by law.