Senior GenAI & ML Solutions Architect

Senior GenAI & ML Solutions Architect

Full-Time 80000 - 100000 Β£ / year (est.) No working from home possible
United States Digital Space LLC

At a Glance

  • Tasks: Lead GenAI initiatives and architect production-grade ML/AI applications.
  • Company: Innovative tech company in London with a focus on AI solutions.
  • Benefits: Competitive salary, mentorship opportunities, and a dynamic work environment.
  • Other info: Collaborate with top professionals and enjoy excellent career growth.
  • Why this job: Become an AI thought leader and make a significant impact in the tech industry.
  • Qualifications: Experience in ML/AI architecture and strong mentoring skills.

The predicted salary is between 80000 - 100000 Β£ per year.

the company in London is seeking a Senior Specialist Solutions Architect (ML & AI) to guide enterprise customers in architecting production-grade ML/AI applications on the Data Intelligence Platform.

You will mentor colleagues and establish yourself as an AI thought leader.

You will lead Gen AI initiatives, work on RAG architectures, agentic systems, AI observability, and natural language querying of structured data, while collaborating with field engineering and solution architects.

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Senior GenAI & ML Solutions Architect employer: United States Digital Space LLC

United States Digital Space LLC is an exceptional employer, offering a dynamic work culture that prioritises innovation and collaboration in the heart of Greater London. With a strong focus on employee well-being and flexible work options, we provide ample opportunities for professional growth and development, making it an ideal environment for those looking to make a meaningful impact in the field of AI-enabled SaaS engineering.

United States Digital Space LLC

Contact Details:

United States Digital Space LLC Recruitment Team

We think you need these skills to ace Senior GenAI & ML Solutions Architect

Machine Learning
Artificial Intelligence
Data Intelligence Platform
GenAI Initiatives
RAG Architectures
Agentic Systems
AI Observability