[Hiring] Opal Platform Operations @Welo Global

Role Description

This role sits at the operational core of our internal platform, Opal, ensuring clients are configured, operational, and supported without requiring direct AI/ML Engineering involvement for routine matters. The ideal candidate is a technical operator with a strong problem-solving mindset, capable of bridging client delivery and platform mechanics. You should be comfortable troubleshooting a broken MT pipeline, presenting performance data in a QBR, and building lightweight automation to eliminate manual work—all within the same week.

Main Responsibilities

• Configure and maintain platform environments across TMS platforms (e.g., XTM, Phrase, memoQ, Trados), including MT engine connections, workflows, AIPE/AIQE parameters, and provider-level settings (glossaries, dictionaries, model selection).
• Act as the primary diagnostics layer between production teams and AI/ML Engineering. Investigate MT quality issues, identify root causes (data, engine, pipeline, or TMS), and escalate with clear reproduction steps and context.
• Manage end-to-end MT operations: data preparation, TM analysis, engine training, evaluation, deployment, and monitoring. Maintain relationships with MT providers and identify opportunities to optimise performance.
• Support pre-sales as an MT/AI subject matter expert. Build ROI models, prepare QBR insights (e.g., edit distance trends, MT savings, cost usage), and advise clients on MT strategy, including engine selection and content suitability.
• Identify and eliminate manual processes through automation. Design or build tools, scripts, and workflows that reduce human intervention and improve scalability. This is a core expectation of the role.

Qualifications

• Hands-on experience with at least one major TMS (XTM, Phrase, memoQ, Trados), including configuration of MT integrations, workflows, and templates.
• Strong understanding of XLIFF/MXLIFF formats and translation pipelines.
• Experience integrating MT providers (e.g., Google AutoML, Microsoft Custom Translator, DeepL, Systran).
• Knowledge of MT customisation, including training data, model types (generic vs custom NMT), and quality drivers.
• Familiarity with MT evaluation metrics (BLEU, TER, edit distance) and human evaluation frameworks.
• Understanding of the MT lifecycle: preprocessing, training, evaluation, deployment, and monitoring.
• Ability to navigate provider platforms and understand their capabilities, limitations, and pricing models.

Requirements

• Experience using Python (or AI-assisted coding tools) to build scripts for data processing (e.g., cleaning TMX files).
• Strong data manipulation skills (e.g., Pandas or equivalent via AI-assisted workflows).
• Basic SQL proficiency (writing, generating, and validating queries).
• Experience building lightweight tools or internal apps (e.g., dashboards, file processors).
• API integration experience: reading documentation, generating code, testing, and debugging.
• Comfortable working in terminal/command-line environments.
• Ability to read and validate code, including AI-generated scripts.
• Strong understanding of data workflows (input → transformation → output).

Data & Reporting

• Excel / Google Sheets (including pivot tables).
• Power BI or similar reporting tools.
• Familiarity with structured data formats: CSV, JSON, XML, TMX.

Other Skills

• Python (advanced).
• LLM / prompt engineering experience.
• Annotation tools (e.g., Label Studio).
• Cloud platforms (GCP, Azure, AWS).
• Jira or similar project management tools.
• Localization industry knowledge (translation workflows, post-editing, quality frameworks, LSP operations).

Competencies

• Bias for action: Prioritises speed and execution over perfection.
• Automation mindset: Constantly identifies opportunities to remove manual work.
• Ownership: Works independently and makes decisions with incomplete information.
• Commercial awareness: Understands how MT impacts revenue, cost, and client value.
• Problem-solving orientation: Focuses on outcomes, not tools.