System Architect - Platform

Lumiere Systems•Houston, TX

About The Position

The Systems Architect is responsible for defining and driving the target platform architecture as the organization modernizes from a landscape of legacy systems and tightly coupled, workflow-heavy applications to a layered platform model with clear boundaries. This role establishes architectural guardrails across the Experience, Orchestration, and ERP layers, champions best practices, and partners with product and engineering leaders to deliver solutions that are scalable, performant, secure, and resilient. The Systems Architect ensures designs are automation-ready and AI-ready (instrumented, observable, well-governed, and designed for safe augmentation) while enabling teams to move quickly without eroding platform integrity.

Requirements

8+ years of experience in software engineering, solutions architecture, or platform architecture, with demonstrated ownership of cross-team architecture decisions.
Proven experience modernizing legacy systems and untangling complex workflows (e.g., monolith-to-services, integration rationalization, phased migrations, strangler patterns).
Deep knowledge of layered architecture and domain boundaries, including API design, integration patterns, and distributed system tradeoffs.
Strong understanding of ERP-adjacent integration considerations (e.g., master data, financial and operational workflows, batch vs. real-time interfaces, change management).
Hands-on experience designing for performance and scale (capacity planning, load testing strategy, caching, async processing, data modeling).
Experience with cloud platforms and modern delivery practices (CI/CD, infrastructure as code, automated testing, containerization/orchestration).
Strong observability mindset (logging, metrics, tracing), with ability to translate operational needs into architecture and standards.
Excellent communication and influence skills: able to align stakeholders, lead architecture reviews, and mentor teams.
Hands-on builder willing to roll up sleeves to help get the initial platform MVP stood up.
Experience mentoring and coaching engineering teams.

Nice To Haves

Experience establishing platform governance: reference architectures, standards, architectural decision records (ADRs), and guardrail tooling.
Experience with event streaming and messaging platforms (e.g., Kafka-like patterns), schema governance, and asynchronous workflow orchestration
Background in data architecture and governance (catalog, lineage, quality, MDM), especially as it relates to analytics and AI use cases.
Experience designing systems for safe AI augmentation (e.g., RAG patterns, tool/function calling boundaries, evaluation/monitoring, human-in-the-loop workflows).

Responsibilities

Define and govern the platform architecture: establish and maintain reference architectures, principles, standards, and decision records for the platform and its integration patterns.
Drive clear layer boundaries: define responsibilities, contracts, and guardrails for the Experience, Orchestration, and ERP layers (e.g., what belongs where, what is prohibited, and how exceptions are handled).
Modernize workflows safely: analyze complex end-to-end workflows across legacy systems, identify coupling and risk, and create pragmatic decomposition and migration paths that preserve business continuity.
Design for scalability and performance: create architectures that meet SLOs/SLAs, handle peak demand, optimize latency and throughput, and apply caching, asynchronous processing, and data-partitioning strategies as appropriate.
Champion API-first and event-driven integration: standardize service contracts, versioning, idempotency, error handling, and integration patterns; reduce point-to-point integrations through reusable platform capabilities.
Enable automation-ready operations: ensure systems can be provisioned, deployed, and operated via automation (IaC, CI/CD, policy-as-code, automated testing, automated rollback and recovery).
Enable AI-ready foundations: design systems with strong observability, data quality, metadata, lineage, and access controls; define patterns for safe AI augmentation (e.g., retrieval over governed data, human-in-the-loop controls, prompt/tooling boundaries).
Establish reliability and resilience: design for graceful degradation, fault isolation, multi-region strategies (where relevant), and disaster recovery; lead architecture reviews for high-risk changes.
Strengthen security and compliance by design: embed identity, authorization, secrets management, encryption, auditability, and secure SDLC practices into platform standards.
Partner and influence: collaborate with product, engineering, ERP owners, and business stakeholders to align roadmaps; mentor engineers and architects; facilitate architecture reviews and ADR discipline.
Measure and improve: define architecture health metrics (e.g., coupling, reuse, deployment frequency, incident rates, lead time for change) and continuously improve based on outcomes.