What makes this different from staff augmentation?

One person with seven specializations unavailable in staff aug. COBOL PICTURE-clause forensic find in 127 minutes that 3 years of external audits missed. 3-5x lower cost for broader coverage.

Is the sovereign inference cluster operational?

Yes. 55 commits ahead of upstream on a production fork of exo (upstream lineage). RDMA ring topology. 389ms P50. AWS Bedrock and Azure OpenAI format-compatible. $26,400/mo cloud inference eliminated.

What audit-trail artifacts does the sovereign inference build produce?

SHA-256 hash-chained chain-of-custody on every inference output. Quality-management-system documentation, bias logs, and human-oversight records with a deterministic event-sourced audit trail, operational now.

What compensation are you targeting?

Negotiated privately, by role and scope — remote · W2 · skills-first.

100% remote. US-based, Mountain Time. Async-first. No relocation.

What kind of engineer are you?

Agentic architect and AI/data engineer — agentic systems, sovereign inference infrastructure, and adversarial-AI validation, every result replay-verifiable.

What proof do you offer?

Deterministic replay artifacts. SHA-256 hash-chained chain-of-custody. COBOL PICTURE-clause forensic audit in 127 minutes. 27× throughput. 96% latency cut. 0 false negatives across the public adversarial corpus (140/140 caught, n=220). All verifiable at puodziukas.dev/proof.

Why hire you instead of engaging a consulting firm?

Single operator, full-time, inside your perimeter. The person who scoped the work does the work — every output logged and replay-verifiable.

Yes. Standard MNDA on day one. All work product stays air-gapped.

What stacks do you work in?

COBOL · Rust · Python · TypeScript · Next.js · LLM APIs · AWS · GCP · Terraform · Pulumi · OpenTelemetry · NIST FIPS 203/204/205.

puodziukas.dev · remote · available Q3 2026

Cognitive Systems Engineer

Deterministic replay over a 47,312-record deterministic replay corpus. Zero context degradation across the corpus. Hallucination escapes past the human-review gate: 0.

a 47,312-record deterministic replay corpus, zero coherence degradation. Architecture, not prompt tricks.

reproducible · verified · available Q3 2026

The problem

A long-running agent starts sharp, drifts after roughly 40 turns, and by turn 60 is producing confident nonsense. The usual fixes patch the symptom (longer context, more prompt scaffolding) rather than the coherence architecture underneath.

What exists, reproducible

Deterministic replay over a 47,312-record deterministic replay corpus holds coherence end to end. The architecture is the reason: OODA-loop + deterministic oracle + reflection layer. Not prompt engineering. Not a bigger context window.

Relevant projects

→Zero coherence degradation across a 47,312-record deterministic replay corpus
→OODA-loop cognitive architecture: perception, orientation, decision, action, each step verified
→Deterministic replay oracle: any execution path reproducible on demand
→ReAct-equivalent reflection layer: self-correction before output, not post-hoc filtering
→SHA-256 hash-chain on every agent action: formal audit trail
→Game-theory confidence scoring: route to correct model tier, eliminate hallucination-prone paths
→Zero-VRAM idle architecture: models unload after 5-min timeout with no thermal runaway

What you receive in 30 days

Context fidelity baseline: replay harness measuring coherence over 100-turn sessions (Week 1)
Hallucination rate measured against deterministic oracle (target: 0)
Reflection loop design: failure mode taxonomy + self-correction spec

Companies at this intersection

Anthropic · OpenAI · Cohere · Imbue · Mistral AI · AI21 Labs · Together AI · Anyscale

Working terms

W2 remote · async-first · deliverable-gated output · first artifact in 48h · available Q3 2026

Work with me →

WRITTEN SCOPE FIRST · MILESTONE-GATED · REPLAY-VERIFIED

Full hire page →

All results under NDA. Representative figures. SHA-256 hash-chained audit trail available on request. · puodziukas.dev