Applied AI Architect & Engineer — I turn frontier AI into safe, reliable, production systems that deliver measurable business outcomes for enterprises.
I help organizations move from use-case discovery through architecture, prototyping, evaluation, and sustained production adoption. I work equally well as a senior technical advisor to CTO/CIO/CISO stakeholders and as a hands-on engineer writing code, building evaluation harnesses, and resolving complex integrations.
- Enterprise AI architecture — end-to-end solutions spanning models, applications, data, integration, security, privacy, governance, evaluation, and deployment.
- Hands-on implementation — Python-first applied engineering: agents, retrieval (RAG), tools, embedding pipelines, APIs and SDKs, with TypeScript/Rust/Go across the stack.
- Prototype → production — reference implementations, POCs and proofs of value that become reliable, observable, cost-aware production systems.
- Evaluation & reliability — systematic evals using representative data, graders, production signals, and human judgment; rigorous debugging, observability, latency and cost tuning.
- Leadership & scale — technical account planning, pre-sales discovery, building technical champions, and enabling partner/ecosystem teams with playbooks and reusable patterns.
| Project | What it demonstrates |
|---|---|
| promptsheon | Git-native, versioned and auditable infrastructure for autonomous AI agent configurations — agentic systems, reproducibility, governance. |
| agent-passport | Identity and provenance for AI agents — secure, auditable agent operations in production. |
| agent-guard | Safeguards for agentic behavior — reliability and safety controls for deployed agents. |
| delta-search | Retrieval and search over changing corpora — RAG foundations, evaluation, and production indexing. |
| underwrite | Applied decision systems on production data pipelines — evaluation, reliability, and measurable outcomes. |
| fleetpilot | Distributed systems and event-driven backends — resilience and scale for cloud-native platforms. |
| Category | Skills |
|---|---|
| Languages | Python, TypeScript, Rust, Golang, SQL |
| AI / LLM | OpenAI API, Codex, agents, retrieval (RAG), evals, prompt engineering, PyTorch, Hugging Face, vLLM |
| Cloud & Infra | AWS, GCP, Kubernetes, Docker, FastAPI, serverless & event-driven architectures |
| Data & Observability | Postgres, Redis, Weaviate, Elasticsearch, OpenTelemetry, streaming |
| Enterprise | Secure & regulated deployments, identity/access, data governance, cost & performance optimization |
- Solution architecture for enterprise customers and large-scale digital-native businesses, aligning AI investments to business outcomes.
- Technical leadership — acting as the senior technical owner across a portfolio, owning technical account plans, adoption priorities, and expansion.
- Security & governance — designing and deploying within enterprise security, privacy, and data-governance requirements.
- Ecosystem enablement — producing reference architectures, workshops, and reusable patterns so partners and internal teams can self-serve at scale.
Most of my work is public. See the odd experiment, the production systems, and the research prototypes above — I default to building repeatable patterns rather than one-off solutions.
- ✉️ sachncs@gmail.com
- Always happy to collaborate on ambitious, weird, or world-changing projects.