AI Solution Engineer

Franklin Fitch; Greater London; On-site; GBP 90,000 - 140,000; Full time

Job summary

Franklin Fitch seeks an AI Solutions Engineer to design, prototype and deliver AI-enabled solutions for enterprise and public-sector clients, with a focus on NVIDIA Technologies and ecosystem. The role involves translating complex business challenges into secure, scalable proofs of concept and production-ready deployments across cloud, on‑premise and hybrid environments.

You will collaborate with AI leadership and delivery teams to turn emerging technologies into practical business outcomes,

Qualifications

• Experience developing LLM and generative AI applications.
• API integration and workflow automation.
• RAG architecture and document intelligence.
• Docker and Kubernetes.
• Git, CI/CD and production deployment.
• Ability to rapidly develop technical demonstrations, POCs and prototypes.
• Understanding of data preparation, model evaluation and observability.
• Experience taking AI concepts from prototype through to production.

Responsibilities

• Design and build AI use cases and proofs of concept for strategic clients.
• Prototype agentic workflows, copilots and AI automation solutions.
• Develop and optimise inference pipelines for large language models and multimodal AI.
• Build secure, governed and enterprise-ready AI demonstrations and pilots.
• Work closely with AI leadership, solution architects and delivery teams to shape technical proposals.
• Create reusable AI accelerators, blueprints and reference implementations.
• Present technical solutions to client stakeholders and senior leadership.
• Support R&D experiments, benchmarking and platform evaluations.
• Contribute to deployment patterns across cloud, sovereign cloud and hybrid environments.
• Implement responsible AI practices, including guardrails, evaluation and observability.
• Translate business and technical requirements into scalable AI architectures and working solutions.

Skills

LLM development

Generative AI

API integration

Workflow automation

RAG architecture

Observability

Docker

Kubernetes

Git CI/CD

Azure experience

Tools

TensorRT-LLM

TensorRT

Triton Inference Server

CUDA-X Data Science

cuDF

RAPIDS

NVIDIA NIM

NVIDIA NeMo Framework

NVIDIA AgentIQ Toolkit

NeMo Guardrails

NVIDIA Cosmos

NVIDIA cuOpt

NVIDIA Metropolis

NVIDIA Riva

NVIDIA ACE

Job description

AI Solutions Engineer – Generative AI & NVIDIA Technologies

We are currently looking to recruit an AI Solutions Engineer to design, prototype and deliver AI-enabled solutions that demonstrate measurable value for large enterprise and public-sector clients.

The role will focus on translating complex business challenges into secure, scalable proofs of concept, reusable AI accelerators and production-ready deployment patterns, with a particular emphasis on NVIDIA's enterprise AI technologies and ecosystem.

Please note: The successful candidate will join an independent technology organisation that uses NVIDIA technologies as part of its AI solution development and client delivery capabilities.

Role Summary

We are seeking an experienced AI Engineer / Solutions Engineer to design and build prototypes, proofs of concept and production-ready AI solutions for enterprise and public-sector clients.

The role requires hand‑on experience with NVIDIA's AI stack, generative and agentic AI frameworks, and enterprise deployment practices, combined with the ability to translate client requirements into working technical demonstrations and scalable solutions.

The successful candidate will work across AI engineering, solution development and client advisory, helping to turn emerging AI technologies into practical business outcomes.

Key Responsibilities

• Design and build AI use cases and proofs of concept for strategic clients.
• Prototype agentic workflows, copilots and AI automation solutions.
• Develop and optimise inference pipelines for large language models and multimodal AI.
• Build secure, governed and enterprise-ready AI demonstrations and pilots.
• Work closely with AI leadership, solution architects and delivery teams to shape technical proposals.
• Create reusable AI accelerators, blueprints and reference implementations.
• Present technical solutions to client stakeholders and senior leadership.
• Support R&D experiments, benchmarking and platform evaluations.
• Contribute to deployment patterns across cloud, sovereign cloud and hybrid environments.
• Implement responsible AI practices, including guardrails, evaluation and observability.
• Translate business and technical requirements into scalable AI architectures and working solutions.

Candidates should have practical experience or strong working knowledge of relevant technologies within the NVIDIA AI ecosystem, including:

Infrastructure, Runtime & Inference Optimisation

• TensorRT-LLM
• TensorRT
• Triton Inference Server
• CUDA-X Data Science
• cuDF
• RAPIDS

AI Microservices & Agentic AI

• NVIDIA NIM
• NVIDIA NeMo Framework
• NVIDIA AgentIQ Toolkit
• NeMo Guardrails
• Reasoning and tool-calling applications
• NVIDIA Cosmos
• Foundation models within agentic and retrieval‑based workflows
• NVIDIA cuOpt
• NVIDIA Metropolis Microservices
• NVIDIA Riva
• NVIDIA ACE

Physical AI, Robotics & Digital Twins

• NVIDIA Isaac / GR00T
• Edge and local inference deployment concepts
• Industrial, physical AI or digital‑twin environments

Core Technical Skills

• Experience developing LLM and generative AI applications.
• API integration and workflow automation.
• RAG architecture and document intelligence.
• Docker and Kubernetes.
• Git, CI/CD and production deployment.
• Ability to rapidly develop technical demonstrations, POCs and prototypes.
• Understanding of data preparation, model evaluation and observability.
• Experience taking AI concepts from prototype through to production.

Strong Azure experience is required, including relevant experience with:

• Azure data and integration services

Candidates should have experience delivering production‑grade AI and data solutions on Azure, including security, observability, performance optimisation, deployment and operational support.

Domain Experience

Experience delivering technology solutions within one or more of the following environments would be beneficial:

• Government and public services
• Financial services, investment banking or hedge funds
• Pharmaceuticals, life sciences or healthcare
• Enterprise operations and shared services
• Other highly regulated industries

Personal Attributes

• Strong problem‑solving and analytical ability.
• Comfortable operating in ambiguous and fast‑moving environments.
• Client‑focused and commercially aware.
• Able to balance experimentation with production discipline.
• Comfortable communicating complex technical concepts to both technical and business audiences.