Applied AI Engineer: Build, Evaluate, and Operate Production LLM

Building production-grade AI systems requires moving beyond basic model prompting to architecting resilient end-to-end applications that span model selection, dynamic retrieval, agentic tool workflows, fine-tuning, and robust MLOps deployment. The course establishes this operational baseline across its curriculum, guiding developers through hybrid-search RAG architectures, multi-step reasoning agents, and systematic evaluation sets. It details how to deploy these pipelines as stable API microservices—optimizing for cost, latency, and throughput via intelligent caching, token limits, and automated CI/CD testing suites that catch hallucinations and quality regressions before code reaches production. The curriculum advances to handling complex multi-sensory data streams with strict integration contracts. The speech integration modules enforce explicit MIME-type and payload boundaries for speech-to-text and text-to-speech endpoints—balancing batch processing against chunked streaming responses to minimize Time To First Sound while managing raw audio byte arrays. Building upon this, multimodal workflows unite speech, vision, and image generation into a single deterministic pipeline. By centralizing routing policies instead of scattering conditional logic across UI callbacks and strictly correlating every intermediate artifact to a single request ID, the architecture prevents silent UI state desynchronization and guarantees a reliable text fallback response even when upstream visual or voice endpoints fail. Finally, the course delivers an operational framework for securing, sanitizing, and monitoring production AI systems. Adversarial safety strategies establish application-layer defenses against prompt injection, jailbreaks, and retrieval poisoning by implementing hard context delimiters, route-specific tool allowlists, and parameter validations that prevent the model from holding final authority over side-effect execution. Data privacy modules map PII hotspots across the entire data lifecycle, enforcing vault-mapped tokenization, dual-stream encrypted logging, and automated retention deletion paths. Bias and fairness concepts convert subjective complaints into scenario-based paired regression tests with human-verifiable criteria, while layered guardrails deploy a defense system—from pre-processing and tool router checks to post-processing, calibrated moderation thresholds, and live telemetry dashboards equipped with emergency kill-switches.