sub-AI (opt) / I. FRONTIER COGNITIVE SYSTEMS

Cognitive Computing Systems

Cognitive AI systems for agent behavior, reasoning, planning, context handling, simulation, and decision support. Senior AI engineer scope covers reasoning workflow, decision policy, feature pipeline, uncertainty handling, evaluator, and review queue, using decision records, historical outcomes, domain rules, exception cases, and human review feedback and measurable checks for calibration, false-positive cost, decision latency, explainability coverage, and out-of-distribution behavior.

Architecture & Risk Blueprint

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

For Cognitive Computing Systems, the AI engineer will map target workflows, data owners, integration points, and known failure modes before design starts
Define reasoning workflow, decision policy, feature pipeline, uncertainty handling, evaluator, and review queue with clear service boundaries, control points, and engineering assumptions
Prepare the dataset, model, runtime, access, and logging requirements needed for Cognitive Computing Systems
Build the evaluation plan for calibration, false-positive cost, decision latency, explainability coverage, and out-of-distribution behavior 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 1.652.388 IDR 28.503.700.000 Request Scope

Controlled Prototype & Evaluation

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

For Cognitive Computing Systems, the AI engineer will implement a controlled prototype around the highest-risk workflow, not a presentation mockup
Connect decision records, historical outcomes, domain rules, exception cases, and human review feedback to a limited runtime using test credentials, sandbox data, and strict access separation
Build the first inference pipeline, scoring service, explanation layer, drift monitor, and decision audit repository slice with traceable inputs, outputs, errors, and reviewer notes
Run evaluation cases for calibration, false-positive cost, decision latency, explainability coverage, and out-of-distribution behavior 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 2.365.438 IDR 40.803.800.000 Request Scope

Recommended

Production Engineering

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

For Cognitive Computing Systems, the AI engineer will build the production service boundary for Cognitive Computing Systems with repository structure and release workflow
Implement inference pipeline, scoring service, explanation layer, drift monitor, and decision audit repository 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 calibration, false-positive cost, decision latency, explainability coverage, and out-of-distribution behavior 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 3.391.530 IDR 58.503.900.000 Request Scope

Operations, Reliability & Governance

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

For Cognitive Computing Systems, the AI engineer will set up operating controls for release approval, model or policy change, incident response, and evidence storage
Define service indicators for calibration, false-positive cost, decision latency, explainability coverage, and out-of-distribution behavior with alert thresholds, review cadence, and escalation roles
Create monitoring for inference pipeline, scoring service, explanation layer, drift monitor, and decision audit repository 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 4.458.203 IDR 76.904.000.000 Request Scope