Alex Sandruk

Alex Sandruk / working model

Forward Deployed
AI Engineering

Customer reality, product judgment, and production AI in one operating loop.

Forward Deployed Engineer Product Engineer AI Engineer

Why this work exists

Models can be bought.
Deployment has to be earned.

The differentiator is not access to intelligence. It is choosing the right workflow, fitting the system to reality, and making it useful under real constraints.

One operating core

Three titles emphasize
different parts of the same work.

01

FDE

Discover the real workflow, earn trust, integrate with the client environment, and own time to value.

02

Product Engineer

Turn ambiguous needs into a coherent product surface and close the loop with actual users.

03

AI Engineer

Build reliable model-driven systems with retrieval, tools, evals, controls, and observable behavior.

The overlap is the position

I work where the customer,
the product, and the system meet.

The operating loop

Start with the work.
Finish with responsibility.

AuditDesignBuildEvaluateDeployObserve

The loop repeats until the system is useful, trusted, and economically justified.

Current state

Documented process is not operating reality.

Emailinconsistent trigger
Spreadsheetmanual re-keying
Internal systemtribal knowledge
Approvalunwritten boundary

The first engineering task is to find the missing context and exceptions.

Workflow drill-down

Trace the actual work before choosing the technology.

Automation boundary

Use the least complicated mechanism that can safely do the job.

Deterministic software

Known rules, stable inputs, predictable transformations.

Agent

Clear objective, variable inputs, tool choice, or flexible path.

Human decision

Material ambiguity, accountability, or irreversible action.

System design

Build over the environment that already exists.

Connect the real sources, preserve identity and permissions, and keep every important action observable.

Evidence-aware workflow and operator system map

Evaluation

Turn non-determinism into evidence.

Golden cases, failure categories, escalation rules, and cost per run make system behavior discussable.

Agent task evaluation with evidence and outcome checks

Deployment

Increase autonomy only after the system earns it.

01Shadowobserve and log
02Drafthuman reviews every action
03Actbounded permissions
04Expandonly with evidence

Logs, rollback, alerts, and escalation are part of the product.

Business case

Technical quality is necessary.
Business movement decides priority.

TIMEcycle time and capacity recovered
RISKerrors, exceptions, and control burden reduced
REVENUEconversion, throughput, or retention improved

An honest audit can also conclude that a workflow should not be automated.

Forward Deployed Engineer

Own the distance between a client problem and a trusted production system.

Workflow discoveryStakeholder communicationIntegration architectureProduction implementationEvaluation and controlsTime to value

Product Engineer

Ship the smallest coherent product that resolves the real user constraint.

Product judgment is choosing what not to build as much as what to ship.

AI Engineer

Make model behavior reliable enough to carry operational responsibility.

Tools, retrieval, structured outputs, evals, tracing, recovery, and cost belong together.

Agent runtime flight recorder showing prompts, tools, and evidence
01 / CLIENT DELIVERY

Construction intelligence
across 20+ sources.

At ScrumLaunch, I coordinated delivery across an AI sales-assistant workflow for a US construction-industry client, spanning TypeScript/Node, Python/FastAPI, and roughly twenty external data sources.

discover the client workflowingest fragmented sourcesverify a reviewable dev/stage baseline
02 / INHERITED COMPLEXITY

No handoff.
A large production system.
A new AI domain.

At Hypetrain, I worked on a mature influencer-marketing SaaS with roughly 0.7M lines of code and 11+ services, contributing to a new AI-assisted creator-selection domain without rewriting the existing core.

~0.7M lines of code11+ services
03 / END-TO-END OWNERSHIP

A live managed SaaS,
from positioning to operations.

I founded and operate useclaw.cloud on open-source agent runtimes: landing, onboarding, workspace provisioning, CI/CD, and the CRM/operations loop behind the service.

Visit useclaw.cloud

The 90-second answer

Explain the work in the order the listener needs.

  1. 01Reality

    What the user or business was actually trying to do.

  2. 02Constraint

    Why the obvious solution was insufficient.

  3. 03Decision

    What architecture and boundary I chose, and why.

  4. 04Evidence

    What shipped, how it behaved, and what changed.

Interview calibration

Confidence is precise ownership, not inflated certainty.

Instead of

“I mostly helped with...”

Say what you owned.
Instead of

“We used some AI...”

Name the system and boundary.
Instead of

“It probably improved...”

Use evidence or state the limit.

Forward deployed / product / AI

Start with a real workflow.
Build the system that earns trust.

I am open to engineering roles where implementation, product judgment, and customer outcomes meet.

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