Solution Architect

SUMMARY STATEMENT We are looking for a Solution Architect to design the technical solutions behind our client engagements and give delivery teams a clear, workable path from concept to production.   You will work across enterprise data, software applications and GenAI - translating complex business problems into practical architectures that delivery teams can build and scale. This could include architecting an agentic workflow for clinical operations, a conversational analytics product grounded in enterprise data, or an AI-enabled decision platform for commercial teams.   You will work directly with clients, define the architecture, test the most important technical decisions yourself and establish the foundations for successful delivery. This is an architecture-first role with meaningful hands-on engineering: you will stay close enough to implementation to prove the architecture works and support it through production delivery, without becoming the primary engineer for every component.   You will also help shape the reusable patterns, technical standards and accelerators behind Lynx’s growing AI-native life sciences practice.     KEY RESPONSIBILITIES   Solution Architecture

• Own the end-to-end solution architecture for client engagements, including data models, system design, integration patterns and technology choices.
• Translate business requirements into clear technical designs and implementation paths that delivery teams can build from.
• Design solutions spanning enterprise data, APIs, applications, cloud platforms and GenAI capabilities.
• Lead technical discovery with clients: understand requirements, assess existing systems and identify dependencies, constraints and delivery risks.
• Present architectural options and trade-offs clearly to technical teams, business stakeholders and senior leaders.
• Make pragmatic decisions across build speed, cost, scalability, security and maintainability.
• Review key implementation decisions and remain involved through delivery to ensure the solution stays aligned with the intended architecture.

GenAI & Applied AI Architecture

• Identify where GenAI can create meaningful business value - and where conventional software, data engineering or machine learning approaches are more appropriate.
• Design GenAI-enabled applications and workflows using patterns such as retrieval-augmented generation, tool calling, agent orchestration and human-in-the-loop review.
• Evaluate model, data and orchestration options based on solution quality, latency, security, cost and operational requirements.
• Define practical approaches for evaluating and monitoring GenAI solutions, including accuracy, relevance, hallucination, reliability and business impact.