AI/ML Specialist Solutions Architect

Team: IT

Location: UK

Commitment: Full-time

Workplace Type: remote

Accountabilities

• Design customer-focused AI and machine learning solutions that maximize business value and align with technical and strategic objectives.
• Act as a trusted technical advisor for customers, helping them successfully adopt and scale AI infrastructure and services.
• Architect and support large-scale AI deployments, including distributed training environments involving multi-node and multi-GPU systems.
• Build strong customer relationships by understanding technical requirements, addressing challenges, and ensuring long-term satisfaction.
• Deliver technical content including presentations, documentation, whitepapers, manuals, and webinars for audiences with different levels of technical expertise.
• Collaborate closely with engineering and product teams to communicate customer feedback, influence priorities, and improve solutions.
• Support customers in moving AI workloads from experimentation and proof-of-concept stages into reliable production environments.
• Stay current with AI/ML technologies, frameworks, and infrastructure trends to provide high-quality technical guidance.

Requirements

• 3+ years of experience working with cloud technologies in MLOps engineering, machine learning engineering, solutions architecture, or similar technical roles.
• Strong understanding of machine learning ecosystems, including models, AI use cases, workflows, and supporting tooling.
• Proven experience designing, deploying, and optimizing distributed training pipelines across multi-node and multi-GPU environments.
• Hands-on experience with machine learning frameworks such as PyTorch, JAX, TensorFlow, Hugging Face, or similar technologies.
• Strong knowledge of cloud infrastructure, DevOps practices, and modern AI deployment approaches.
• Experience with programming languages such as Python, Go, Java, or C++.
• Familiarity with technologies including Kubernetes, Slurm, Docker, Helm, Git, Terraform, and infrastructure-as-code practices.
• Excellent written and verbal communication skills, with the ability to explain complex technical concepts to both technical and non-technical audiences.
• Experience deploying production inference infrastructure or scaling ML pipelines from prototypes to production is considered a strong advantage.

Benefits

• Competitive compensation package.
• Opportunity to work on impactful AI infrastructure projects shaping the future of machine learning.
• Flexible work options, including remote opportunities across Europe.
• High level of ownership and autonomy in a fast-growing technology environment.
• Career development opportunities with continuous learning and exposure to advanced AI technologies.
• Collaboration with talented international teams of engineers and AI specialists.
• Innovative culture focused on experimentation, growth, and meaningful impact.