The **AI Quality Engineer** is responsible for defining, implementing, and operating quality practices for AI/ML-enabled products and platforms—ensuring models, data, and AI-powered features behave reliably, safely, and measurably across real-world conditions. The role blends software quality engineering with ML evaluation, data validation, and production monitoring to prevent regressions, reduce risk, and increase customer trust in AI-driven capabilities.

The **AI Policy Engineer** designs, operationalizes, and enforces responsible AI and AI governance requirements as **technical controls** across the AI/ML lifecycle—turning policy intent (legal, risk, ethics, security, product) into **deployable engineering mechanisms** (policy-as-code, pipeline gates, automated evaluations, documentation automation, and audit-ready evidence). This role exists in software and IT organizations because modern AI systems (especially GenAI) introduce fast-moving risks—privacy, security, safety, bias, IP, regulatory exposure, and brand harm—that cannot be mitigated by documentation alone and must be **engineered into delivery workflows**.

The **AI Platform Reliability Engineer** ensures that the organization’s AI/ML platform (training pipelines, feature/data dependencies, model registry, and online inference/serving) is **reliable, observable, scalable, secure, and cost-effective**. This role applies Site Reliability Engineering (SRE) principles to ML systems, where reliability must account for both classic uptime/latency concerns and ML-specific behaviors like model drift, data quality regressions, and reproducibility.

The **AI Platform Engineer** designs, builds, and operates the internal platform capabilities that enable teams to develop, deploy, and run machine learning (ML) and AI systems reliably in production. This role focuses on creating secure, scalable, developer-friendly “paved roads” for model training, evaluation, deployment, observability, and governance—so product teams and data scientists can deliver AI features faster with less operational risk.

The **AI Guardrails Engineer** designs, builds, and operates technical controls (“guardrails”) that make AI systems safer, more reliable, policy-compliant, and predictable in production. This role focuses on preventing and detecting harmful, insecure, non-compliant, or low-quality AI behavior—especially in **LLM-powered** features, agentic workflows, and AI-assisted user experiences.

The AI Governance Engineer designs, implements, and operates the technical controls that ensure AI/ML systems are safe, compliant, auditable, and aligned with organizational policy throughout their lifecycle—from data intake and model training to deployment, monitoring, and decommissioning. This role sits at the intersection of engineering, risk, and responsible AI, translating governance requirements into automated guardrails, tooling, and repeatable processes that integrate directly into ML and software delivery pipelines.

The **AI Evaluation Engineer** designs, implements, and operates the evaluation systems that determine whether AI/ML (especially LLM-powered) features are *good enough, safe enough, and reliable enough* to ship and to keep running in production. This role turns ambiguous product intent (“make answers more helpful”) into measurable quality targets, repeatable test suites, and release gates that prevent regressions and reduce AI risk.

The AI Engineer designs, builds, deploys, and operates machine-learning–powered capabilities in production software systems. The role bridges applied ML modeling, data engineering, and software engineering to deliver reliable AI features (e.g., personalization, forecasting, classification, retrieval, ranking, and conversational experiences) that meet business, security, and performance requirements.

The **AI Compliance Engineer** ensures that AI/ML systems are designed, deployed, and operated in a way that meets internal governance standards and external regulatory obligations (e.g., privacy, security, transparency, auditability, fairness, and safety). This role translates policy and regulatory requirements into **engineering-grade controls** embedded across the AI lifecycle—data ingestion, training, evaluation, deployment, monitoring, and incident response.

The **AI Benchmarking Engineer** designs, builds, and operates repeatable evaluation systems that measure the quality, safety, performance, and cost of machine learning (ML) and generative AI models across product use cases. The role exists to ensure model and model-driven features are selected, deployed, and iterated based on **evidence**, not intuition—reducing regressions, accelerating iteration cycles, and enabling trustworthy AI outcomes at scale.

The AI Agent Engineer designs, builds, evaluates, and operates AI “agents” that can plan and execute multi-step tasks using large language models (LLMs), tools/APIs, and enterprise data. This role turns LLM capabilities into reliable product features and internal automations by engineering agent workflows, retrieval-augmented generation (RAG) pipelines, tool integrations, guardrails, and observability.

An **Agent Reliability Engineer (ARE)** ensures that AI agents—LLM-powered systems that plan, call tools, retrieve knowledge, and take actions—operate **reliably, safely, and cost-effectively** in production. This role blends **Site Reliability Engineering (SRE)** discipline with **LLM/agent evaluation, guardrails, and observability**, focusing on the unique failure modes of agentic systems (non-determinism, tool-call brittleness, prompt injection, rate limits, context overflow, and model/provider variability).

The Agent Platform Engineer designs, builds, and operates the internal platform capabilities that enable teams to safely develop, deploy, and monitor AI agents (LLM-powered systems that plan, call tools/APIs, retrieve knowledge, and take actions). This role turns rapidly evolving agent frameworks and model capabilities into reliable, secure, cost-effective, and reusable platform primitives that product and engineering teams can consume through APIs, SDKs, templates, and paved roads.