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RWS

AI-powered language services and localization

Senior AI Engineer

Natural Language Processing & LLMsPosted on Nov 5
Full-timeNo salary listed

About the Job

Requirements

  • Significant software engineering experience, typically 5+ years, building and operating production systems, tools, libraries, or services used by many other engineers, with excellent API design, reliability, and developer experience.
  • Experience with CI/CD and cloud infrastructure.
  • Proficiency in Python and/or another general-purpose programming language, with strong testing discipline.
  • Hands-on experience building with LLMs or other machine-learning systems, including prompt engineering, fine-tuning, retrieval, and model integration, and understanding of their failure modes and tradeoffs.
  • Experience designing and leading evaluation for AI/ML systems, including defining metrics and methodology, building evaluation pipelines, managing test sets, and reasoning rigorously about model quality and regressions.
  • Strong command of evaluation concepts, including accuracy, precision, recall, and F1 metrics; tradeoffs between automated and human evaluation; statistical significance; and the limits of each approach.
  • Excellent written and verbal communication, and a history of influencing technical direction across teams and mentoring other engineers.
  • Comfort with ambiguity and sound judgment in scoping, prioritizing, and sequencing high-impact work with limited direction.

Responsibilities

  • Contribute to the design and architecture of core platform components and evaluation systems, making key technical decisions and taking accountability for reliability, scalability, and long-term maintainability.
  • Help set the technical direction for building, evaluating, and deploying AI capabilities across the company, and define a platform vision that scales beyond the immediate team.
  • Design reusable abstractions, software development kits, and services for model integration, prompt management, experimentation, and deployment that establish organization-wide patterns and reduce duplicated effort.
  • Help define the company-wide evaluation strategy and methodology for AI capabilities, including automated metrics, human-in-the-loop workflows, test-set management, and benchmarking, and establish standards other teams can use.
  • Build evaluation frameworks and developer tooling that are robust enough for production and accessible to non-specialist developers.
  • Establish observability standards for AI systems, including quality, performance, cost, and regression signals, and build dashboards and reporting that turn those signals into actionable decisions.
  • Drive engineering rigor through testing discipline, reproducibility, sound experimental design, and statistically defensible measurement of model quality.
  • Provide technical leadership on ambiguous, high-impact problems by scoping and sequencing work where direction is limited.
  • Mentor engineers and raise engineering standards through code review, design review, and leading by example.
  • Contribute to model and system governance practices, including model cards, system cards, dataset and test-set versioning, reproducibility, and responsible-AI checks embedded in the platform.
  • Codify best practices into tooling and standards adopted by developers across the company.
  • Track developments in LLMs, evaluation research, and AI tooling, and translate them into pragmatic, well-scoped platform improvements.
  • Prototype and de-risk emerging techniques and tools, advancing promising ones from experiment to supported capability.
  • Champion adoption of new platform capabilities across teams and lower the barrier for developers to use them effectively.
  • Keep the platform current and competitive without pursuing novelty for its own sake.
  • Partner with research, product, and localization leaders to align evaluation methodology with real-world quality and customer needs.
  • Influence roadmap and technical strategy beyond the immediate team and build consensus across engineering and product stakeholders.
  • Gather requirements from developers across the company and represent their needs in platform direction as a trusted technical partner.
  • Communicate technical direction, trade-offs, and quality standards clearly to technical and non-technical audiences.

Desired Qualifications

  • Deep experience evaluating NLP, machine translation, or content-generation systems, including metrics such as COMET, chrF++, BLEU, and MetricX, and MQM-style human evaluation.
  • Experience with experimentation and observability tooling, data and test-set versioning, and rigorous benchmarking workflows.
  • Established practice in AI governance and documentation, including model cards, system cards, reproducibility, and responsible-AI considerations, at an organizational level.
  • Broad familiarity with the modern LLM ecosystem, including open and proprietary models, orchestration frameworks, and vector stores, and well-formed views on their tradeoffs.
  • Experience supporting multilingual or localization-focused products at enterprise scale.

About the Company

RWS

AI-powered language services and localization

RWS Holdings plc provides language services and technology solutions on a global scale for sectors including aerospace, defense, automotive, life sciences, and more. Its offerings span localization, content management, artificial intelligence, and intellectual property, delivered through AI-powered language services paired with human expertise. Clients engage through service contracts and technology licensing to enable multilingual digital experiences and streamline processes. The company differentiates itself by serving a wide range of industries with tailored, compliant solutions that integrate AI, content management, and IP capabilities under one umbrella. Its goal is to help businesses overcome language barriers and optimize operations across multilingual environments.

Company size
5,001-10,000
Company stage
IPO
Headquarters
Chalfont St Peter, United Kingdom
Founded
1958

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