AI Platform Engineer

Build

• Design, build, and operate services in the central AI platform. Every service should have clear API contracts, versioning, and SLOs from day one.
• Write production Python for AI services. Contribute to shared libraries, SDKs, and integration patterns that product teams will consume.
• Instrument everything: cost tagging per request, latency and error metrics, quality signals, and audit logs. If it is not measured, it is not shipped.
• Own on-call rotation for the AI platform services you build. Author runbooks and improve them after every incident.

Partner

• Work directly with product engineering leads across the product lines to onboard their AI features onto the central platform.
• Provide technical support, integration guidance, and troubleshooting to product teams consuming platform services.
• Contribute to Architecture Decision Records. Push back on decisions you disagree with; document tradeoffs.

Operate

• Set the operational bar: observability, alerting, incident response, and post-incident reviews.
• Own vendor evaluation for tools in your area of specialization. Run bakeoffs when the choice is not obvious. Make cost, quality, and reliability tradeoffs explicit.
• Contribute to the AI security posture: PII handling, tenant isolation, prompt injection defense, and audit logging within your services.

Applied AI & RAG Engineering

• The AI-forward end of the platform. You build the retrieval, prompt, and guardrail systems that make LLM output good enough to ship to customers.
• RAG-as-a-service platform: ingestion, chunking, embedding, retrieval quality, and hybrid search.
• Prompt engineering at scale: templates, evaluation, versioning, and per-tenant customization.
• Guardrails and content safety: input filtering, output validation, PII redaction, tool-use sandboxing.
• Agent frameworks and tool-use patterns as agent workflows move into production across product lines.
• Domain-specific fine-tuning experiments and quality benchmarking.

Backend & Platform Engineering
The foundation of the AI platform. You own the model gateway, orchestration layer, service mesh, and the data plumbing that ties everything together.

• Multi-provider model gateway with routing, fallback, retry, and rate-limit logic.
• Prompt registry, versioning, and rollout controls (canary, feature flags).
• Shared libraries and SDKs for product-team consumption. API contracts, versioning, deprecation strategy.
• Tenant isolation architecture: how customer data flows through platform services safely.
• Cost attribution and budget enforcement at the gateway layer.

MLOps/ LLMOps Engineering

The operational spine of the AI platform. You own how models are deployed, observed, evaluated, and rolled back safely across the portfolio.

• Observability platform: prompt and response tracing, cost per request, quality signals, drift detection.
• Evaluation infrastructure: golden datasets, offline evals, LLM-as-judge patterns, regression testing.
• Model deployment pipelines, including fine-tuned models where applicable.
• Alerting and SLO framework for AI services. Distinct from general engineering SLOs: quality regression is a first-class alert.
  • Fine-tuning and RLHF pipelines when product-specific tuning becomes justified.