Technology & Engineering

AI Engineer

Builds the software that puts a language model to work inside a product, shaping prompts and retrieval over an embedding store, adapting the model to its domain, then deploying it and proving it behaves under real traffic.

Level: Associate
Requirements: 7
Courses: 34

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Requirements

What the role asks for

Each skill is earned by completing a short list of verifying courses. Set the role as your target in the app and it orders the work for you.

01
AI Safety, Evaluation & Guardrails
Covers methods and best practices for assessing, monitoring, and enforcing the safe and responsible operation of AI systems, including risk evaluation, bias detection, robustness testing, and implementation of technical and procedural guardrails.
Any 3 of 4 courses
02
LLM Application Development
Covers designing, building, and deploying applications that leverage large language models (LLMs) for tasks such as text generation, summarization, conversational interfaces, and integration with external data or APIs.
Any 3 of 5 courses
03
MLOps & Model Deployment
Covers the processes, tools, and best practices for deploying, monitoring, and maintaining machine learning models in production environments, including CI/CD pipelines, model versioning, and automated retraining workflows.
Any 3 of 5 courses
04
Model Fine-Tuning & Adaptation
Covers techniques for adjusting pre-trained machine learning or deep learning models to improve performance on new, domain-specific datasets, including methods for transfer learning, hyperparameter tuning, and domain adaptation across various architectures.
Any 3 of 5 courses
05
Prompt Engineering
Prompt Engineering covers designing, optimizing, and evaluating prompts to effectively interact with large language models (LLMs) and generative AI systems for tasks such as code generation, data extraction, and content creation.
Any 3 of 5 courses
06
Supervised & Unsupervised Learning
Covers the principles and practical techniques of supervised and unsupervised machine learning, including model selection, training, evaluation, and clustering, with applications in data classification, pattern recognition, and predictive analytics.
Any 3 of 5 courses
07
Vector Databases & Embeddings
Covers designing, implementing, and querying vector databases for storing and retrieving high-dimensional embeddings, including integration with machine learning workflows and optimizing similarity search for applications like recommendation systems and semantic search.
Any 3 of 5 courses