AI Engineer

• Build containerized AI services in Python. Implement clean APIs where needed and standards-based integrations for enterprise systems.
• Design retrieval & agent flows using industry-standard frameworks; implement prompt/tool versioning and safe rollouts (e.g., feature flags, canary).
• Guardrails & governance: help implement controls around PII handling, audit logging, RBAC, prompt-injection defenses, and egress controls.
• Evaluation automation: create eval harnesses, golden sets, regression gates, and basic business KPIs (e.g., quality, safety, latency, cost).
• Observability: instrument tracing/metrics/logging with standard tooling, integrate with enterprise monitoring/logging platforms, and build actionable dashboards/alerts.
• Operational rigor: contribute to runbooks and incident hygiene. Participate in the on-call rotation for the AI services you help own.
• CI/CD: use pipeline-as-code for delivery and keep code-quality/security gates clean for frequent deployments.
• Team play: embed with asset teams when appropriate. Contribute back reusable components, SDKs, and docs to the AI engineering platform.

AI Engineer

Director Applied AI Engineering

Enterprise Data & AI Architect

AI Architect

Senior Applied AI Engineer