Principal OR Engineer (Optimization & Decision Intelligence)

As a Principal AI Architect, you will define and govern the end-to-end technical architecture of Makkook’s Optimization & Decision Intelligence platform(s), and ensure we can deliver new customer implementations fast (target: ≤ 8 weeks per use case after core platform maturity).Core Responsibilities:Platform Architecture & Technical StrategyDefine platform architecture: modular components, data contracts, integration patterns, deployment topology (cloud/on-prem), and security/observability standards.Design reusable “platform primitives”: canonical entities (resources/tasks/time/cost), constraint packs, objective templates, scenario manager, and explainability layer.Make solver/stack decisions (build vs buy), and establish technical roadmaps with the CTO.Optimization & Decision Engine LeadershipLead modeling and solution strategy for key families:Production planning & scheduling (sequence-dependent setups/changeovers, minimum run lengths, maintenance windows)Workforce allocation and shift planningRouting, dispatching, and fleet assignmentInventory/replenishment and supply chain planningSet standards for model formulation, performance, scalability, and stability (runtime budgets, warm starts, decomposition, heuristics/hybrids where needed).Build a repeatable benchmarking harness and KPI framework (cost, service, risk, feasibility rate, runtime).Feasibility, Diagnostics & ExplainabilityOwn the approach to infeasibility detection and repair (constraint relaxation strategies, IIS where applicable, actionable diagnostics).Ensure decisions are explainable to operations teams (constraints hit, trade-offs, what changed vs previous plan, reason codes).Delivery & Engineering ExcellencePartner with PM/Delivery to define scope, milestones, acceptance criteria, and technical risk management.Guide integrations with ERP/WMS/TMS/CMMS/SCADA and production data pipelines.Establish OptOps practices: versioning, monitoring, runtime regressions, rollback, and quality gates.Technical LeadershipMentor AI engineers (including those coming from NLP/CV backgrounds) into OR/Optimization craftsmanship.Run architecture reviews, ADRs, and enforce best practices across code, models, and deployments. 7+ years in applied optimization / operations research / decision intelligence, with hands-on delivery experience.Strong track record in building and deploying optimization systems using one or more of:MILP / MIP, CP/CP-SAT, decomposition, heuristics/metaheuristics, hybrid strategies.Ability to translate real operations into formal models (capacities, time windows, setup/changeover, batching, minimum run lengths, resource calendars, SLAs).Solid software engineering fundamentals for production systems (clean architecture, testing, CI/CD mindset, performance profiling).Experience integrating decision engines into real enterprise environments (APIs, event-driven patterns, data pipelines, on-prem constraints).Clear communication: can explain complex trade-offs to both engineers and operators.Hands-on experience in: OR-Tools (CP-SAT), Gurobi/CPLEX, Pyomo, JuMP, or similar.Experience with feasibility diagnostics (IIS, relaxation, reason codes, repair recommendations).Experience in building “re-optimization loops” (daily/shift updates, warm starts, rolling-horizon planning).Observability/monitoring: Prometheus/Grafana (or equivalent), structured logging, runtime regression monitoring.Preferred to have domain exposure in manufacturing planning, logistics routing, retail replenishment, or supply chain optimization.