AI Engineering

Maps AI product types to risk levels and sets the stage for everything that follows.

Decomposes AI problems into evaluable tasks with clear success criteria.

The evaluation bible — defines quality, measurement, and shipping discipline across all AI systems.

Clarifies what correctness means and how to agree on it.

Turns quality into measurable dimensions and thresholds.

Structures prompts and tools for reliability and control.

Builds and evaluates retrieval-augmented generation systems end to end.

Designs and evaluates multi-step agent systems that take real-world actions.

Chooses the right model for each task and tier.

Balances recall, privacy, and context budgets.

When to fine-tune, data requirements, safety checks, evaluation gating, and ROI.

Builds, validates, and maintains evaluation datasets at production scale.

Runs labeling programs — tooling, workforce, quality control, and cost management.

Builds the systems and processes that make expert review consistent and scalable.

Engineers the automated evaluation infrastructure — judges, runners, and CI integration.

Full-stack AI security: OWASP LLM & Agentic Top 10, prompt injection, model theft, data poisoning, supply chain attacks, zero trust, SOC 2, ISO 27001/42001, EU AI Act, and NIST AI frameworks.

Runtime telemetry, drift detection, alerting, incident response, reliability engineering, and operational excellence for enterprise AI systems including agents, RAG, and multi-model architectures.

CI/CD integration, golden set automation, deployment gating infrastructure, cost controls, safety regression, and business KPI gates.

Serving infrastructure, model versioning, feature flags, canary rollouts, runtime routing, caching strategies, rate limiting, and rollback systems for enterprise AI deployment.

Fallback chains, circuit breakers, graceful degradation, incident response, chaos engineering, and resilience patterns for AI systems.

Latency engineering, perceptual UX, ASR/TTS evaluation, conversational quality, barge-in handling, real-time safety, streaming architecture, session state integrity, conversational recovery, cost governance, voice biometrics, and enterprise voice operations for real-time AI systems where milliseconds matter.

Finds the failures users and attackers will exploit before they do.

Locale routing, translation strategy, multilingual eval slices, cultural safety, and regional compliance.

Keeps inference cost aligned with business reality.

Optimizes quality without destroying margins or latency.

Scales eval infrastructure — pipelines, tooling, compute, and platform ops.

Multi-region deployment, Kubernetes for AI, GPU orchestration, edge inference, training infrastructure, and platform operations at global scale.

Adapts evaluation to customer-specific expectations at scale.

Connects evals to policy, audit, risk controls, team structure, and accountability.

Profit improvement, margins, SLAs, cost-to-serve, and contract pricing for AI products.