sub-AI (opt) / II. DISTRIBUTED & AUTONOMOUS INTELLIGENCE

Real-Time Autonomous Systems

Distributed AI services for orchestration, coordination, federated operation, edge intelligence, and monitored execution. Senior AI engineer scope covers AI service architecture, model workflow, data boundary, integration contract, and operational control, using representative enterprise workflows, approved datasets, model behavior traces, and user interaction records and measurable checks for task accuracy, failure handling, latency, drift signals, human review paths, and production readiness.

Architecture & Risk Blueprint

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

For Real-Time Autonomous Systems, the AI engineer will map target workflows, data owners, integration points, and known failure modes before design starts
Define AI service architecture, model workflow, data boundary, integration contract, and operational control with clear service boundaries, control points, and engineering assumptions
Prepare the dataset, model, runtime, access, and logging requirements needed for Real-Time Autonomous Systems
Build the evaluation plan for task accuracy, failure handling, latency, drift signals, human review paths, and production readiness 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 1.710.661 IDR 29.508.900.000 Request Scope

Controlled Prototype & Evaluation

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

For Real-Time Autonomous Systems, the AI engineer will implement a controlled prototype around the highest-risk workflow, not a presentation mockup
Connect representative enterprise workflows, approved datasets, model behavior traces, and user interaction records to a limited runtime using test credentials, sandbox data, and strict access separation
Build the first model serving layer, orchestration service, API boundary, evaluation harness, and monitoring pipeline slice with traceable inputs, outputs, errors, and reviewer notes
Run evaluation cases for task accuracy, failure handling, latency, drift signals, human review paths, and production readiness 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 2.435.304 IDR 42.009.000.000 Request Scope

Recommended

Production Engineering

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

For Real-Time Autonomous Systems, the AI engineer will build the production service boundary for Real-Time Autonomous Systems with repository structure and release workflow
Implement model serving layer, orchestration service, API boundary, evaluation harness, and monitoring pipeline 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 task accuracy, failure handling, latency, drift signals, human review paths, and production readiness 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 3.478.788 IDR 60.009.100.000 Request Scope

Operations, Reliability & Governance

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

For Real-Time Autonomous 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 task accuracy, failure handling, latency, drift signals, human review paths, and production readiness with alert thresholds, review cadence, and escalation roles
Create monitoring for model serving layer, orchestration service, API boundary, evaluation harness, and monitoring pipeline 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 4.562.852 IDR 78.709.200.000 Request Scope