Senior GenAI Solutions Engineer

Senior GenAI Solutions Engineer

Full-Time 80000 - 98000 Β£ / year (est.) No working from home possible
Kindred (London) Limited

At a Glance

  • Tasks: Own the delivery of complex GenAI use cases and design LLM-backed services.
  • Company: Join FDJ UNITED, a leader in innovative AI solutions.
  • Benefits: Competitive salary, flexible work options, and opportunities for mentorship.
  • Other info: Dynamic team environment with great potential for career advancement.
  • Why this job: Shape the future of AI while working on cutting-edge technology.
  • Qualifications: Experience in AI solutions and strong collaboration skills required.

The predicted salary is between 80000 - 98000 Β£ per year.

FDJ UNITED is seeking a Senior AI Solutions Engineer to own end-to-end delivery of complex Gen AI use cases on the KAIT platform, staying hands-on while shaping design and evaluation.

You will design LLM-backed services, lead RAG design, ensure observability, cost/performance optimization, and collaborate with Platform Engineering, Data Engineering, and Security to deploy on Kubernetes.

Mentor peers and communicate trade-offs to product stakeholders, aligning with governance and compliance.

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Senior GenAI Solutions Engineer employer: Kindred (London) Limited

At Kindred (London) Limited, we pride ourselves on fostering a collaborative and innovative work culture that empowers our employees to thrive. As a People Systems Analyst, you will not only play a pivotal role in enhancing Workday's functionality but also benefit from extensive professional development opportunities and a supportive team environment. Located in the vibrant city of London, we offer a dynamic workplace where your contributions directly impact thousands of employees, making your work both meaningful and rewarding.

Kindred (London) Limited

Contact Details:

Kindred (London) Limited Recruitment Team

We think you need these skills to ace Senior GenAI Solutions Engineer

GenAI Use Case Development
LLM Design
RAG Design
Observability
Cost/Performance Optimisation
Kubernetes Deployment
Collaboration with Platform Engineering