sub-AI (opt) / V. SIMULATION & SYNTHETIC INTELLIGENCE

AI Simulation & Scenario Modeling

Simulation and synthetic intelligence services for data generation, scenario modeling, digital twin design, and decision testing. Senior AI engineer scope covers scenario engine, synthetic data pipeline, simulation clock, validator, environment model, and reporting layer, using scenario library, synthetic data rules, historical events, constraint sets, and adversarial test cases and measurable checks for scenario realism, synthetic data bias, coverage gaps, stress behavior, and decision sensitivity.

Architecture & Risk Blueprint

Senior AI engineering discovery for architecture, risk boundaries, data readiness, and implementation scope.

For AI Simulation & Scenario Modeling, the AI engineer will map target workflows, data owners, integration points, and known failure modes before design starts
Define scenario engine, synthetic data pipeline, simulation clock, validator, environment model, and reporting layer with clear service boundaries, control points, and engineering assumptions
Prepare the dataset, model, runtime, access, and logging requirements needed for AI Simulation & Scenario Modeling
Build the evaluation plan for scenario realism, synthetic data bias, coverage gaps, stress behavior, and decision sensitivity so acceptance is measurable, not impression-based
Document risks around unsafe action, data leakage, dependency failure, integration drift, unclear accountability, and evidence gaps and turn them into mitigation tasks with named owners
Deliver architecture diagrams, runbooks, test records, release notes, acceptance criteria, and engineering backlog for procurement, technical review, and implementation approval

USD 2.012.875 IDR 34.722.100.000 Request Scope

Controlled Prototype & Evaluation

A limited working system to test behavior, integration boundaries, evaluation criteria, and operational limits.

For AI Simulation & Scenario Modeling, the AI engineer will implement a controlled prototype around the highest-risk workflow, not a presentation mockup
Connect scenario library, synthetic data rules, historical events, constraint sets, and adversarial test cases to a limited runtime using test credentials, sandbox data, and strict access separation
Build the first simulation runner, data generator, validation harness, experiment tracker, and replayable result archive slice with traceable inputs, outputs, errors, and reviewer notes
Run evaluation cases for scenario realism, synthetic data bias, coverage gaps, stress behavior, and decision sensitivity and record pass, fail, and uncertain outcomes
Review security, data handling, prompt or policy behavior, and integration limits before production planning
Produce a prototype report with architecture changes, blocked items, engineering estimates, and release criteria

USD 2.952.012 IDR 50.922.200.000 Request Scope

Recommended

Production Engineering

Production implementation with service boundaries, pipelines, evaluation harness, security controls, and handover.

For AI Simulation & Scenario Modeling, the AI engineer will build the production service boundary for AI Simulation & Scenario Modeling with repository structure and release workflow
Implement simulation runner, data generator, validation harness, experiment tracker, and replayable result archive with environment separation, secrets handling, API contracts, and rollback path
Engineer data preparation, model routing, policy checks, test fixtures, and operational telemetry for the service
Create automated evaluation jobs for scenario realism, synthetic data bias, coverage gaps, stress behavior, and decision sensitivity before every accepted release
Add audit records, access controls, monitoring operational intelligence interfaces, incident notes, and dependency version tracking
Hand over diagrams, runbooks, acceptance records, deployment notes, and backlog items to the owner team

USD 4.314.336 IDR 74.422.300.000 Request Scope

Operations, Reliability & Governance

Operating model for reliability, monitoring, release control, evidence records, and long-term technical governance.

For AI Simulation & Scenario Modeling, the AI engineer will set up operating controls for release approval, model or policy change, incident response, and evidence storage
Define service indicators for scenario realism, synthetic data bias, coverage gaps, stress behavior, and decision sensitivity with alert thresholds, review cadence, and escalation roles
Create monitoring for simulation runner, data generator, validation harness, experiment tracker, and replayable result archive including latency, errors, drift, cost, version changes, and abnormal behavior
Run tabletop recovery checks for unsafe action, data leakage, dependency failure, integration drift, unclear accountability, and evidence gaps and document the decision path for urgent rollback or shutdown
Prepare governance records for access review, data retention, evaluation evidence, vendor dependency, and stakeholder reporting
Maintain an engineering roadmap for reliability, cost, security, and capability improvements after launch

USD 5.734.632 IDR 98.922.400.000 Request Scope