Build reliable AI-enabled services using software engineering, APIs, RAG, evaluation, security, and production operations.
AI Engineers build dependable software services around foundation models, enterprise data, retrieval, APIs, security controls, evaluation, and production monitoring.
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Build services, integrations, tests, and utilities.
Connect models and applications to enterprise systems.
Ground answers in authorized enterprise knowledge.
Measure retrieval, grounding, quality, permissions, and failure behavior.
Protect data, identities, tools, and application boundaries.
Deploy, observe, version, and operate AI services.
Roadmap, tools, projects and interview preparation — free for everyone.
Python, APIs, Git, SQL, testing, and service design.
Understand model APIs, prompt behavior, limitations, and structured outputs.
Build retrieval, metadata, grounding, and authorization.
Create datasets, regression checks, permissions, and guardrails.
Deploy, monitor, trace, handle failures, and manage change.
Application development and evaluation.
Model and enterprise-service integration.
RAG knowledge lookup.
Version control and collaboration.
Application packaging.
Deployment, security, monitoring, and scaling.
Define users, decision, risk, data, and measurable success.
Choose model, application, retrieval, identity, and integration boundaries.
Prepare content, metadata, retrieval logic, and permission filtering.
Connect models to application logic and controlled APIs.
Test behavior before release and observe it in production.
Customer-service reps must search many policy documents, and confident AI answers can still be unsupported or unauthorized.
Build a policy assistant that retrieves approved evidence, enforces access, evaluates behavior, and escalates risky cases.
Build a controlled internal assistant that retrieves approved policy evidence, respects document permissions, escalates high-risk cases, and records operational evidence.
Application, model, retrieval, identity, data, and monitoring components.
Ingestion, chunking, metadata, retrieval, source citation, and permission filtering.
Grounding, retrieval, authorization, and failure cases.
User-to-document access logic and restricted-data handling.
Quality, latency, errors, security signals, and change review.
Demonstrates system design.
Demonstrates enterprise knowledge integration.
Shows measured behavior.
Shows security reasoning.
Shows production thinking.
Cover ingestion, metadata, retrieval, permissions, citations, and evaluation.
Discuss evidence, retrieval, evaluation, uncertainty, and limitations.
Discuss application metrics, AI quality, traces, errors, security, and feedback.
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Build practical analyst foundations around security operations, incidents, cloud security and Security+ concepts.
Learn governance, risk, compliance, controls, AI governance and responsible-AI practices used in modern organizations.
Learn requirements, Agile, Jira, SQL, UAT and practical AI-assisted business-analysis workflows.
