CI/CD pipelines (GitLab, GitHub Actions, CodePipeline), infrastructure as code (Terraform, Terragrunt, CloudFormation), container platforms (ECS, EKS, AKS, Kubernetes), and the boring reliability work that makes production boring. Blue/green and canary deploys, zero-downtime migrations, automated rollback.

Internal LLM applications, retrieval-augmented generation (RAG) systems, document intelligence pipelines (OCR + extraction + classification), agent orchestration. Built on your data, deployed in your VPC. Bedrock, OpenAI-compatible endpoints, on-prem inference — we pick the right substrate for the constraints.

From data pipeline to model serving. SageMaker, Bedrock, custom inference endpoints. MLOps that doesn't require a PhD to maintain. Domain-specific models for document understanding, computer vision, and structured prediction.

Multi-account AWS organizations (Control Tower, Organizations, SCPs), Azure landing zones (CAF, Blueprints), GCP folders and projects. IAM least-privilege, cost guardrails, audit-ready logging, monthly cost optimization reviews. We've stood up 200+ accounts across federal and commercial.

OpenTelemetry instrumentation, Prometheus + Grafana, CloudWatch, X-Ray, distributed tracing. SLOs that mean something, error budgets that get enforced, runbooks that get used. Incident response, post-mortems, chaos engineering when warranted.

FedRAMP-adjacent practices, ATO documentation, CVE/CWE remediation, secret scanning, SBOM generation, supply chain hardening. We speak compliance without slowing delivery. Across multiple federal agencies and regulated industries — references available under NDA.

SAST, DAST, SCA, container scanning (Trivy, Anchore), secret detection, IaC scanning (Checkov, tfsec). Pipeline-as-code with security gates. SonarQube, Fortify, Twistlock, or whatever stack you already have.

S3 architecture, lake formation, ETL/ELT, change data capture, event streaming (Kafka, Kinesis, EventBridge). PostgreSQL, DynamoDB, Redis, vector stores. The boring infrastructure that AI workloads actually need underneath.

Get engineering teams actually using AI in their daily work. Tool selection, prompt engineering patterns, code-review workflows with AI assistants, internal copilots, training programs. We work alongside your team until the habits stick — not a one-off workshop.

Model Context Protocol servers, agent-to-agent workflows, tool-use integrations. We build the connective tissue between LLMs and your existing systems — APIs, databases, internal tools — so agents can actually do work, not just talk about it.

Bedrock, Azure OpenAI, on-prem inference gateways, API key management, model routing, BYOK (bring-your-own-key) patterns, tenant isolation. Rate limiting, abuse detection, audit logs. Enterprise controls for LLM access without strangling developer velocity.

Prompt-injection defense, PII detection, jailbreak mitigation, output validation, hallucination checks. Evaluation pipelines (LLM-as-judge, golden sets, regression tests). Guardrails that catch real failures without blocking legitimate use.

Training data curation, document ingestion for RAG, vector index management, embedding pipelines, data versioning. The unglamorous plumbing that makes AI features actually work in production — versioned, monitored, and recoverable.