Sr​/Distinguished Architect, Continuous Quality & Release Engineering

Our client is an AI-first enterprise planning software company on a mission to be the most value-creating platform for enterprises. Its platform integrates siloed planning capabilities and captures significant value leakage, helping global industry leaders plan smarter and faster and optimize their supply chains.

Location: Remote (US-based)

Our client is an AI-first enterprise planning software company on a mission to be the most value-creating platform for enterprises. Its platform integrates siloed planning capabilities and captures significant value leakage, helping global industry leaders plan smarter and faster and optimize their supply chains.

We are looking for an exceptional senior engineer to define and build the change-safety architecture for the company. The fundamental question for this role is: what are we about to change, what could it break, and what evidence is required to promote, hold or recover?

You will turn quality and release assurance into a continuously learning software-engineering system. The scope spans code, configuration, schema, models, infrastructure, deployment, customer-specific state, upgrades and post-release verification. This is a hands-on builder-architect role. You will set technical direction, write and review critical code, build the assurance primitives, and lead through technical influence across Reliability & Operations, Platform, Applications, AI, Sec Ops and Services/FDE.

Every significant change should receive an assurance plan proportional to its actual risk and customer exposure. AI can understand changes and generate tests; deterministic oracles, exact state, policy and recovery determine whether evidence is sufficient to release.

• Own the architecture for a change-safety loop across development, Continuous Integration (CI), shared pre-production, company-hosted environments and customer-managed environments, grounded in a precise model of code, configuration, schema, models, infrastructure, dependencies, deployments and feature controls.
• Drive change-impact analysis and dynamic assurance planning: use diffs, dependency structure, historical defects, production usage, current coverage and customer exposure to decide what evidence a specific change actually requires.
• Build AI-generated and AI-maintained executable tests, fixtures, mocks, synthetic data and fault injection, paired with deterministic test oracles and validators. Generated artifacts must be readable, reviewable, versioned and governed like production code.
• Treat test infrastructure and suite quality as an engineering system: flake, weak oracles, duplicate coverage, stale tests and excessive runtime are defects to be measured and repaired.
• Deliver customer-specific lifecycle assurance that binds evidence to the exact tested state and workload, distinguishes platform regression from customer incompatibility, and can run analyzers locally when customer data cannot move.
• Lead release and upgrade orchestration, residual-risk statements, post-deployment verification and explicit recovery; escaped defects feed back into impact models, tests and release policy.

• Learn the software-delivery and customer lifecycle deeply: code and configuration paths, test infrastructure, shared pre-production, release and upgrade mechanics, customer-specific extensions, escaped-defect classes and current AI-led QA and upgrade automation assets.
• Establish a common taxonomy and baseline for changes, escaped defects, suite signal, flake, runtime, release failure, upgrade success, recovery readiness and customer impact.
• Define the initial change model, exact-state assurance contract, data-boundary patterns, evaluation…