Senior AI Engineer

Box (NYSE:BOX) is the leader in Intelligent Content Management. Their platform enables organizations to fuel collaboration, manage the entire content lifecycle, secure critical content, and transform business workflows with enterprise AI. Founded in 2005, Box serves leading global organizations including JLL, Morgan Stanley, and Nationwide. Headquartered in Redwood City, CA, with offices across the United States, Europe, and Asia.

• 5+ years software engineering experience; 2+ years building ML/AI products or agent systems.
• Strong Python skills; familiarity with Go, Java, and TypeScript preferred.
• Experience with LLMs, prompt engineering, RAG, and vector stores (FAISS, Milvus, Weaviate).
• Experience with ML frameworks (PyTorch, TensorFlow) and model evaluation/validation techniques.
• Cloud experience with AWS, Azure, GCP; Kubernetes, Docker, Terraform.
• Experience with observability and monitoring tools (Prometheus, Grafana, ELK, OpenTelemetry).
• Familiarity with security and compliance (IAM, encryption, GDPR, HIPAA awareness).

Preferred qualifications:

• Experience with LangChain, Ray, or agent frameworks.
• Familiarity with semantic metadata/catalog tooling (Dataplex, Looker).
• Experience with CI/CD (GitHub Actions, Jenkins), chaos testing, and load testing.
• Track record of reducing hallucinations and improving model reliability.
• Experience with cloud data warehouses (BigQuery, Snowflake, Redshift).
• Google Cloud Platform, BigQuery administration, data access/security modeling, and regulated data classification experience.

• Design, build, and deploy AI agents that automate and improve IT and business workflows while minimizing hallucinations and ensuring reliable outputs.
• Implement model validation, monitoring, and fail-safe mechanisms to detect and reduce incorrect or risky agent behavior.
• Architect secure data handling pipelines (ingest, storage, access controls, encryption) to protect sensitive information used by models and agents.
• Develop and maintain MCP servers and developer tooling that surface actionable insights and make it easy for teams to query, debug, and act on model outputs.
• Integrate observability and telemetry (logging, metrics, tracing) for agents and infrastructure to enable rapid incident detection, root-cause analysis, and performance tuning.
• Build role-based APIs and interfaces that allow business and engineering teams to safely interact with agents and automated workflows.
• Create onboarding, documentation, and training materials to help teams adopt agents and follow best practices for secure, low-risk usage.
• Collaborate with security, compliance, and product teams to define policies, access controls, and approval workflows for model deployment and data use.
• Optimize infrastructure cost and reliability through capacity planning, fault tolerance, CI/CD pipelines, and automated testing for agent behavior and integrations.
• Continuously evaluate new model architectures, tooling, and defenses to improve accuracy, reduce bias, and maintain a secure, scalable AI platform.
• Define canonical schemas, metadata standards, and classification taxonomies to enable trusted decisions and secure data sharing between IT and business teams.
• Create automated metadata pipelines using AI/NLP to extract, normalize, and enrich dataset context (ontology, definitions, sensitivity, lineage), and automate metadata population, semantic matching, and lineage inference for faster cataloging.
• Develop AI agents and tooling that summarize dataset health, recommend joins/transformations, infer lineage, and route remediation tasks to owners.
• Measure and report on adoption, data health, and governance maturity; iterate standards and tooling based on feedback and telemetry.
• Mentor peers and evangelize best practices to raise organizational data maturity and enable reliable AI/analytics at scale.
• Troubleshoot production issues.
• Participate in cross-functional design and review processes.
• Conduct regular security reviews and compliance audits.