Senior / Lead Platform Forward Deployed Engineer - Cogentiq I2C

It's fun to work in a company where people truly BELIEVE in what they are doing! We're committed to bringing passion and customer focus to the business. About Cogentiq I2C Cogentiq I2C is Fractal's agentic AI platform for invoice-to-cash operations, spanning Collections, Cash Application, Deductions, Credit Risk, and Invoice Management. The platform runs on a four-tier architecture: a Next.js application layer, FastAPI services, the Cogentiq APA agentic orchestration layer, and a data estate built on Azure Databricks and PostgreSQL. Deployments land in client Azure tenancies against live ERP data (Dynamics 365, SAP), which means deployment engineering is a first-class discipline, not an afterthought to development. Why this role, now The platform has crossed from being built to being deployed. Collections is entering pilot with enterprise clients, and the deployment machinery is real: Databricks Asset Bundles with one-click, script-based deployment; auto-loader ingestion running historical and incremental loads into a common data model; versioned bundles and wheel-file packages delivered through Git and Azure Artifactory; and a quick-start standard under which a newcomer must be able to stand up the full data platform by running one setup script. This role carries that machinery into client environments and makes it hold under client governance, client data, and client timelines. It owns everything between our release artefacts and a running, observable, correctly governed installation; candidates who want to work on agent behaviour and LLM integration should apply to the AI FDE track instead. What you will do Own the deployment architecture of client installations from environment readiness through hypercare: bundle strategy, environment topology (Dev, QA, production; per-client catalogs), release sequencing, and sign-off that the installation meets the validation standard. Navigate Unity Catalog and platform governance Client and internal IT control catalog and metastore provisioning, storage decisions, and access models. You will assess what their governance model means for our setup scripts, negotiate provisioning paths, and know when a shared-infrastructure convenience creates an SOW, NDA, or data-isolation problem that must be escalated rather than worked around. Set and enforce the platform engineering standards: idempotent, rerunnable deployments; fail-fast error handling with no silent except-and-continue paths; environment-variable-driven parameterisation with a single point of change per client value; and configuration conventions that hold across Databricks today and Fabric tomorrow. Own the shared utilities architecture: interface-function contracts and factory-pattern abstractions across Databricks, Fabric, PostgreSQL, SQL Server, and storage back ends, packaged as versioned wheel files through Azure Artifactory, so swapping a client's platform is a configuration change, not a rewrite.