Context: huge portfolio of shipped production AI systems (RAG, LLM, multi-agent, AWS),
self-taught, no big-tech logo. Goal: Lead/Principal AI or Data/Platform Architect.
1. Architecture casebook — 3–5 flagship system pages: data → embed → retrieve → LLM → tools → output diagram, trade-off rationale (pgvector vs OpenSearch vs Pinecone; Swarm vs Conductor), reliability/eval, scale. This is THE senior differentiator.
2. Company-specific Agent Stack teardown — use the editorial newsletter engine to research the target company's AI stack and publish an issue analyzing how you'd improve it; send to the hiring manager. Proof-of-work + hyper-personalization in one.
3. "I built your job posting as a demo" — pick a role, build a tiny working agent that does what the JD asks, deploy it, send the link. Almost nobody does this.
4. Direct outreach to hiring managers/teams — use the outreach engine; reference their actual product + link a relevant flagship demo. Bypass recruiters.
5. Quantify impact even solo — "24+ Lambdas, 23 DynamoDB tables, 70 CloudFront distros; RAG over 1,200 docs; ~50% LLM cost cut via prompt caching; idempotent S3 caching." Cost-engineering = principal signal.
6. 3-minute Loom narrating the architecture + trade-offs of one system; attach to every application.
7. Open-source one crisp piece (dedup ledger, two-host audio pipeline, or the RAG retrieval service) with a great README — GitHub proof + name SEO.
8. Eval + safety writeup — golden datasets, schema/JSON enforcement, guardrails, approvals for risky actions, red-team scenarios. Separates principal from mid.
9. The self-evolving-system narrative — TITAN as a multi-agent OS that improves itself + ships products nightly is genuinely rare. Lead with it.
10. Live demos in the portfolio — recruiters can click into working apps (already done at portfolio.silentinfinity.com).