The **Associate AI Platform Engineer** helps build, operate, and continuously improve the internal platform capabilities that enable data scientists and ML engineers to train, evaluate, deploy, and monitor machine learning models reliably in production. This role focuses on implementing well-defined components (infrastructure, CI/CD automation, model packaging, deployment workflows, observability hooks, and guardrails) under the guidance of senior engineers, while building strong foundational skills in MLOps and platform engineering.

The Associate AI Evaluation Engineer designs, implements, and operates repeatable evaluation processes that measure the quality, safety, and reliability of AI systems—most commonly large language model (LLM) features, retrieval-augmented generation (RAG) experiences, and classical ML components embedded in software products. The role focuses on building evaluation harnesses, curating test datasets, defining metrics and acceptance criteria, and turning model behavior into actionable engineering and product decisions.

The **Associate AI Engineer** is an early-career engineering role within the **AI & ML** department responsible for building, integrating, testing, and operating AI-enabled software components under the guidance of more senior engineers. The role focuses on turning well-scoped model and data requirements into reliable code, reproducible experiments, and production-ready artifacts (APIs, batch jobs, pipelines, monitoring hooks) that support AI features in products and internal platforms.

The Associate AI Agent Engineer builds, tests, and operates “agentic” AI capabilities—software components that use large language models (LLMs) plus tools, memory, retrieval, and orchestration to complete multi-step tasks reliably inside products and internal workflows. This role focuses on implementing well-scoped agents, improving their accuracy and safety, and integrating them into production services with strong observability and evaluation practices.

The **Applied AI Engineer** designs, builds, and ships AI-driven capabilities into production software systems, turning model prototypes and research outcomes into reliable, observable, secure, and cost-effective product features. The role sits at the intersection of software engineering, machine learning engineering, and product delivery—owning the “last mile” of applied AI: integration, deployment, evaluation, and operational excellence.

The **AI Security Engineer** designs, implements, and operates security controls that protect AI/ML systems across the full lifecycle—data, training, evaluation, deployment, inference, and monitoring. The role focuses on preventing and detecting AI-specific threats (e.g., data poisoning, model theft, prompt injection, insecure tool use in agents, supply-chain compromise) while integrating with standard application and cloud security practices.

The **AI Safety Engineer** designs, implements, and operates technical safeguards that reduce harm from machine learning (ML) systems—especially modern generative AI and LLM-enabled features—while preserving product usefulness and performance. The role blends software engineering, applied ML evaluation, security-minded threat modeling, and governance-aware delivery to ensure AI systems behave reliably under real-world usage, misuse, and adversarial conditions.

The AI Reliability Engineer ensures that AI/ML-powered products and platforms are dependable in production—meeting reliability, latency, cost, and quality targets while remaining safe and observable under real-world usage. This role blends Site Reliability Engineering (SRE) practices with ML operations realities (non-determinism, data drift, model/version sprawl, and rapidly evolving dependencies).

The **AI Red Team Engineer** proactively identifies, validates, and helps mitigate security, safety, and misuse risks in AI systems—especially **LLM-powered products**, AI agents, and ML-enabled features—before those risks impact customers or the business. The role blends adversarial engineering, applied security testing, and practical ML/LLM understanding to uncover failure modes such as jailbreaks, prompt injection, data leakage, harmful content generation, and tool/agent misuse.

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.