sub-AI (opt) / VII. CYBER-PHYSICAL & EMBEDDED AUTONOMY

Mission-Critical AI Systems

Embedded and cyber-physical autonomy services for sensor fusion, robotics, industrial systems, and real-time operation. Senior AI engineer scope covers sensor pipeline, edge runtime, real-time decision loop, safety interlock, telemetry channel, and fallback mode, using sensor streams, device constraints, environmental scenarios, command rules, safety limits, and operator feedback and measurable checks for real-time response, sensor noise tolerance, fallback behavior, command safety, and field reliability.

Architecture & Risk Blueprint

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

For Mission-Critical AI Systems, the AI engineer will map target workflows, data owners, integration points, and known failure modes before design starts
Define sensor pipeline, edge runtime, real-time decision loop, safety interlock, telemetry channel, and fallback mode with clear service boundaries, control points, and engineering assumptions
Prepare the dataset, model, runtime, access, and logging requirements needed for Mission-Critical AI Systems
Build the evaluation plan for real-time response, sensor noise tolerance, fallback behavior, command safety, and field reliability 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 2.535.241 IDR 43.732.900.000 Request Scope

Controlled Prototype & Evaluation

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

For Mission-Critical AI Systems, the AI engineer will implement a controlled prototype around the highest-risk workflow, not a presentation mockup
Connect sensor streams, device constraints, environmental scenarios, command rules, safety limits, and operator feedback to a limited runtime using test credentials, sandbox data, and strict access separation
Build the first edge inference service, sensor fusion module, command validator, telemetry bridge, and field-test logging system slice with traceable inputs, outputs, errors, and reviewer notes
Run evaluation cases for real-time response, sensor noise tolerance, fallback behavior, command safety, and field reliability 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 3.607.710 IDR 62.233.000.000 Request Scope

Recommended

Production Engineering

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

For Mission-Critical AI Systems, the AI engineer will build the production service boundary for Mission-Critical AI Systems with repository structure and release workflow
Implement edge inference service, sensor fusion module, command validator, telemetry bridge, and field-test logging system 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 real-time response, sensor noise tolerance, fallback behavior, command safety, and field reliability 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 5.167.136 IDR 89.133.100.000 Request Scope

Operations, Reliability & Governance

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

For Mission-Critical AI 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 real-time response, sensor noise tolerance, fallback behavior, command safety, and field reliability with alert thresholds, review cadence, and escalation roles
Create monitoring for edge inference service, sensor fusion module, command validator, telemetry bridge, and field-test logging system 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 6.790.330 IDR 117.133.200.000 Request Scope