Senior AI Engineer

Job Purpose

The Senior AI Engineer is an individual contributor who defines technical direction while driving the quality, scalability, and reliability of next-generation AI-powered systems. This role operates at the intersection of research, software engineering, and advanced testing, transforming cutting-edge ideas into robust, production-ready platforms.

 

This is a senior individual contributor leadership role: the Senior AI Engineer operates as a force multiplier, shaping architecture and core platforms and frameworks, guiding teams, and also pioneering and designing research projects that are evaluated, presented, and then delivered at enterprise scale with high confidence.

 

About RWS

 

RWS is a global AI solutions company empowering the world’s most trusted enterprise AI. 

 

Our proprietary Cultural Intelligence Layer, powered by 250,000 data specialists, cultural and language experts and deep domain professionals, backed by 45+ patents, makes enterprise AI culturally fluent, contextually accurate and secure, ensuring every interaction reflects a brand’s tone, context and customer values.

Through our Generate, Transform and Protect segments, we deliver intelligent content, enterprise knowledge, large-scale localization and IP protection for global growth. Trusted by 80+ of the world’s top 100 brands, RWS provides the confidence, governance and expertise organizations need to deploy AI safely, responsibly and at scale.

 

Headquartered in the UK, RWS is listed on AIM (RWS.L). 

 

More information: rws.com. 

 

About AI Platforms and Excellence

 

The AI Platforms and Excellence team aims to accelerate the development of external-facing, product-ready AI capabilities that materially differentiate RWS offerings, improve customer outcomes, and drive revenue growth across the business.

 

The team provides a centralized, execution-focused capability that productizes AI at scale. It delivers reusable platforms, proven patterns, and clear guardrails so product teams can rapidly ship secure, high-quality, and commercially relevant AI features, consistently and sustainably.

 

With a global reach, RWS provides technology and services to over 7500 end users worldwide. Our core functions encompass Enterprise & Technical Architecture, Network & Voice, Infrastructure, Service Delivery, Service Operations, Data & Analytics, Security & Quality Compliance, Transformation, Application Development, and Enterprise Platforms.

 

Job Overview

Key Responsibilities

 

Architecture and technical strategy

• Contribute to the design and architecture of core platform components and evaluation systems, making the load-bearing technical decisions and bearing accountability for their reliability, scalability, and long-term maintainability.
• Help set the technical direction for how AI capabilities are built, evaluated, and deployed across the company, and define a coherent platform vision that scales beyond your immediate team.
• Design reusable abstractions, SDKs, and services for model integration, prompt management, experimentation, and deployment that establish organization-wide patterns and reduce duplicated effort.

Research and delivery excellence

• Help define the evaluation strategy and methodology for AI capabilities across the company – automated metrics, human-in-the-loop workflows, test set management, and benchmarking – and establish the quality standards other teams build against.
• Build evaluation frameworks and developer tooling robust enough for production yet simple enough for non-specialist developers to adopt.
• Establish observability standards for AI systems – quality, performance, cost, and regression signals – and build dashboards and reporting that turn those signals into actionable decisions.
• Drive engineering rigor in delivery through testing discipline, reproducibility, sound experimental design, and statistically defensible measurement of model quality.

Technical leadership

• Provide technical leadership on the team's most ambiguous and highest-impact problems, 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 documentation (model cards, system cards), dataset and test-set versioning, reproducibility, and responsible-AI checks embedded directly into the platform.
• Act as a technical multiplier – codifying best practices into tooling and standards adopted by hundreds of developers.