Master GenAI engineering disciplines.

Build production RAG & prompt chain applications, design streaming chat UIs, implement guardrails & evaluation, optimize LLM inference costs, and deploy on Kubernetes.

Build autonomous multi-agent systems with planning, reasoning, tool use, memory, MCP/A2A protocols, safety boundaries, and production evaluation.

Design enterprise GenAI reference architectures, create ADRs and technical standards, bridge GenAI with enterprise workflows.

Build RAG data pipelines for ingestion, chunking, embedding, and indexing. Manage vector store operations and embedding model lifecycle.

Architect multi-provider LLM gateways, implement semantic caching and batch optimization, monitor provider SLAs, and optimize inference costs.

Build internal GenAI developer platforms with self-service capabilities, multi-tenancy, RBAC, CI/CD for model/prompt/guardrail pipelines.

Monitor hallucination rates and token costs, operate guardrails and eval gates, manage prompt versioning and canary deployments.

Engineer defenses against prompt injection, jailbreaks, and data exfiltration. Implement PII leakage detection, content safety, and compliance.

Rapid-prototype GenAI solutions on customer infrastructure, integrate GenAI with customer data and workflows, scope solutions with delivery methodology.

Design automated LLM evaluation pipelines, red-team GenAI systems, build bias detection and fairness benchmarks, implement guardrails.

Scope GenAI solutions with estimation, risk, and success criteria. Orchestrate delivery teams, manage client relationships.

Hire and build GenAI engineering teams, design team structures for GenAI, set engineering quality frameworks.