About the role
We are looking for an experienced AI Solution Engineer to act as the link between PwC’s industry architects, who identify AI business cases, and the firm’s development capabilities. This role shapes AI solutions, meets client needs, and delivers measurable business value and technically feasible solutions.
The AI Solution Engineer is a key technical resource responsible for selecting, designing, demonstrating, and delivering prototype solutions that align with customer needs and business goals during early engagement.
As a trusted advisor, the Solution Engineer has a strong mix of technical expertise, problem-solving skills, and business acumen to effectively create cloud prototypes to demonstrate and communicate the value of solutions to both technical and non-technical stakeholders.
They provide experience in identifying viable and feasible AI solutions to address specific client issues and demonstrate PwC’s technical capability as part of proposals. They demonstrate extensive knowledge of hyperscaler AI offerings, understanding all of the technical requirements and dependencies of a product/solution and explaining them to potential clients
What your days will look like;
Primarily sales-oriented, this role is focused on supporting the sales process by bridging the gap between technical teams and non-technical salespeople. The role helps sales teams select and prospects understand the AI/ML solution, demonstrates how it solves their business problems, and assists in demonstrating AI technical excellence to the client in the early sales process
This role is for you if you have;
Extensive experience working with sales teams and shaping viable and feasible software solutions as part of process change for enterprise customers
AI Model Architecture – Evidenced expertise in LLMs, SLMs, (Large Language Models/Small Language Models) performance, suitability, training requirements, and deployment
Cloud & Hyperscalers – Practical experience with at least two of the following is required: AWS Bedrock/Sagemaker, Google Vertex AI, OpenAI and Azure ML is required
An understanding of underlying statistics, machine learning and data science, data engineering and big data concepts is required
An understanding of the process and data complexities and prerequisites, and success criteria needed to deliver a solution that meets user and business needs
Cloud Security & FinOps – Knowledge of landing zones, security, and AI cost optimisation
