Senior AI Engineer
RWS
Senior AI Engineer
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. The role involves architecture, research, technical leadership, and innovation.
Senior AI Engineer
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. The role involves architecture, research, technical leadership, and innovation.
Salary
Core Qualifications
Technical (Must-have)
Soft Skills
Preferred Qualifications
Technical (Nice-to-have)
Key Responsibilities
- Contribute to the design and architecture of core platform components and evaluation systems.
- Help set the technical direction for how AI capabilities are built, evaluated, and deployed across the company.
- Design reusable abstractions, SDKs, and services for model integration, prompt management, experimentation, and deployment.
- Help define the evaluation strategy and methodology for AI capabilities across the company.
- 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 and build dashboards and reporting.
- Drive engineering rigor in delivery through testing discipline, reproducibility, sound experimental design, and statistically defensible measurement.
- Provide technical leadership on the team's most ambiguous and highest-impact problems.
- Mentor engineers and raise engineering standards through code review, design review, and leading by example.
- Contribute to model and system governance practices including documentation, dataset and test-set versioning, reproducibility, and responsible-AI checks.
- Act as a technical multiplier by codifying best practices into tooling and standards.
- Track developments in LLMs, evaluation research, and AI tooling, and translate them into pragmatic improvements.
- Prototype and de-risk emerging techniques and tools, shepherding promising ones from experiment to supported capability.
- Champion the adoption of new platform capabilities across teams.
- 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.
- Gather requirements from developers across the company and represent their needs in platform direction.
- Communicate technical direction, trade-offs, and quality standards clearly to both technical and non-technical audiences.