AI Roles

Automates business workflows by combining AI models, tools and integration platforms.

Ships AI products directly with customers, working on-site or embedded to deploy real systems fast.

Designs and demos AI solutions for customers; pairs deep technical skill with client work.

Supports and builds with customers hands-on, often on AI and cloud platforms.

Owns the ongoing technical relationship between a company and its key customers.

Deploys AI tools into customer organizations and makes them work in practice.

Designs technical solutions for customers, increasingly around AI and cloud platforms.

Builds the pipelines and infrastructure that move and store data at scale.

Analyzes data and builds statistical and ML models to answer business questions.

Designs interfaces and interactions for AI-powered products, from chat to agents.

Helps developers build with AI platforms through docs, samples, talks and community.

Applies AI research and models to concrete product and customer problems.

Builds applications and products on top of AI models, APIs and modern ML infrastructure.

Builds AI systems that use tools, reason across tasks and operate inside structured workflows.

Trains, optimizes and productionizes machine learning models and pipelines.

Builds systems that generate text, images, audio or video with generative models.

Designs end-to-end AI system architecture: models, data, infrastructure and cost.

Specializes in large language models: fine-tuning, serving, retrieval and inference performance.

Builds the infrastructure that trains, deploys and monitors machine learning reliably.

Designs and optimizes the instructions and contexts that steer AI model behavior.

Reviews and scores AI model outputs to improve quality, accuracy and safety.

Teaches AI systems domain knowledge through examples, feedback and structured data work.

Positions and sells AI products: product marketing, sales engineering and market strategy for AI.

Advises organizations on AI strategy, adoption and implementation.

Runs the operational backbone of AI teams: data ops, labeling ops and model ops coordination.

Owns AI product strategy, from model capabilities and data to user-facing tradeoffs.

Manages technical products and platforms, including AI/ML-powered surfaces.

Implements and scales research ideas into working models and systems.

Engineers technical safeguards, evaluations and mitigations for AI system risks.

Builds the policies, reviews and controls that keep AI use compliant and responsible.