Senior Ml Solutions Architect

Description:

Senior ML Solutions Architect (AI Inference)

United States 

The Company

Our client is a well-funded cloud platform provider building critical infrastructure for the global AI ecosystem. They offer a serverless AI inference platform that enables businesses to deploy and scale open-source Large Language Models without the burden of managing GPU infrastructure or building specialised in-house ML teams. Backed by significant investment and led by a world-class engineering team, they are scaling rapidly across the US and internationally.

The Role

This is a high-impact, customer-facing technical role sitting at the intersection of ML engineering and solutions architecture. You will work directly with customers to design, build, and deploy production-grade applications on the company’s serverless LLM inference platform. You’ll be the technical bridge between the platform and its users — architecting custom solutions, driving adoption, and feeding critical insights back to the product and engineering teams.

What You’ll Do

• Design and implement bespoke LLM-based solutions using serverless AI inference services to solve real business problems and drive customer outcomes.
• Build and demonstrate production-ready applications leveraging LLM APIs across multimodal capabilities (text, vision, audio) and domain-specific models.
• Provide expert technical guidance on prompt engineering, RAG architectures, model selection, and inference optimisation.
• Collaborate closely with product and engineering teams to relay customer feedback and shape the platform’s roadmap.
• Guide customers through the full lifecycle from proof-of-concept through to production ensuring solutions are performant, reliable, and cost-effective.

What You’ll Bring

• 5+ years of experience in ML/AI systems, with at least 2 years focused on LLMs and generative AI.
• Deep knowledge of the LLM ecosystem: model architectures, training, and fine-tuning methodologies.
• Hands-on experience with prompt engineering, end-to-end LLM pipeline development, and evaluation.
• Practical experience with agentic frameworks (LangChain, LangSmith, smolagents, or equivalents).
• Experience implementing vector databases and RAG patterns in production environments.
• Strong Python proficiency and experience deploying LLM applications via APIs.

Nice to Have

• Experience with inference frameworks such as vLLM, SGLang, TensorRT-LLM, or Transformers.
• Knowledge of inference optimisation techniques: quantization, dynamic batching, KV caching, model routing.
• Experience with multimodal AI models (vision-language, speech).
• Proficiency with Docker, Kubernetes, and MLOps tooling.
• Open-source ML/AI contributions.