Localization Lead, Engineering & Automation

About us:

Apertera is building a dedicated Language Technology & Workflow Automation team to help accelerate and scale how our Professional Translation business operates. This team will improve productivity, quality, and scalability across intake, quoting, project setup, translation workflows, linguistic assets, QA, reporting, and system integrations.

We are looking for a senior technical lead who can understand translation operations, drive automation initiatives, and help establish and manage a small agile team that turns operational friction into reliable, auditable tooling.

This role sits at the intersection of translation operations, language technology, automation, and AI-enabled workflow design. Much of our work is high-precision financial, legal, and securities translation, where quality, confidentiality, repeatability, and auditability matter. The role is not about applying AI for its own sake; it is about using the right combination of language technology, workflow automation, linguistic assets, operational data, and AI-enabled tools to improve real production workflows.

What You'll Do

Automation & roadmap ownership

• Own and prioritize the roadmap for Professional Translation (PT) workflow automation and internal tooling, ensuring initiatives are practical, scalable, and tied to clear operational outcomes.
• Identify and deliver automation across the production lifecycle: intake and quoting, file analysis, project setup, assignment logic, vendor and resource workflows, QA tracking, and production reporting.
• Build and operationalize automation and workflow orchestration across Professional Translation, connecting production systems into reliable, auditable workflows that reduce manual effort and meet client confidentiality and security requirements.

Linguistic-asset leverage

• Treat translation memories, terminology, and related linguistic assets as core productivity and quality assets.
• Drive improvements in TM leverage, fuzzy-match optimization, terminology consistency, and reuse, especially in high-repetition financial, legal, and securities content.
• Direct specialist work related to maintaining, segmenting, improving, and measuring the performance of linguistic assets.

Data systems & integration

• Unlock value from operational data across Plunet, Phrase, Hub Spot, Jira, ATAI, finance, QA ticketing, and other tools to improve visibility, decision-making, and measurable impact.
• Improve data flow and integration across core production systems to reduce duplicate entry, strengthen operational visibility, and support consistent handoffs.
• Partner with the broader Technology organization on architecture, security, and shared infrastructure so Professional Translation tooling fits the company’s wider technical environment.

Team leadership & delivery

• Help establish and manage a small agile team focused on localization engineering, automation, linguistic data, analytics, and workflow tooling, in close partnership with Technology leadership.
• Establish delivery cadence, quality standards, and adoption practices appropriate for tooling used in a high-precision translation environment.
• Work directly with Linguistic Operations leadership to surface workflow gaps, quality risks, and automation opportunities, and translate them into clear technical requirements and delivery plans.
• Ensure solutions are designed for real adoption by PMs, quoting, resources, linguists, revisors, DTP, QA, and finance — not just technical completeness.
• Define and track success metrics tied to reduced manual work, improved TM and terminology leverage, stronger QA visibility, faster turnaround, better consistency, and operational scalability.
• Identify where internal workflow improvements, automation solutions, QA tools, integrations, or operational innovations may have broader productization potential for Apertera AI clients.

• Automation & data engineering. Hands-on background in automation, workflow orchestration, internal tooling, or data engineering, including scripting, working with APIs, connecting disparate systems, and building reliable operational workflows. Experience with tools such as n8n, Apache Airflow, or similar platforms is an asset.
• Language technology domain. Direct experience in the language technology ecosystem, such as a CAT/TMS environment (e.g., memoQ, Phrase, RWS/Trados, Smartcat), a machine translation or language AI platform, or a technical role within an LSP with hands-on involvement in translation workflows.
• Translation memory &…