Solution Architect - GenAI, Data & Applications (US)

About the Company

Responsibilities

• ● ing 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.
• ● 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.
• ● Establish appropriate security, privacy, guardrail and responsible AI patterns for enterprise and regulated environments.
• ● Hands-On Validation & Delivery Enablement
• ● Build targeted prototypes, proofs of concept and early application or pipeline components to validate the most important architectural decisions.
• ● Test model behavior, retrieval quality, system integrations and user workflows before the delivery team builds at scale.

Requirements

• ● Bachelor's degree in Computer Science, Engineering or a related field, or equivalent practical experience.
• ● -8+ years of experience across data engineering, software engineering and solution or systems architecture.
• ● A track record of designing data models, system architectures and integration patterns for production systems-not just diagramming them.
• ● Hands-on proficiency in at least one modern language, such as Python or TypeScript, and comfort prototyping application or pipeline components personally.
• ● Strong grounding in data engineering fundamentals, including pipeline design, data modeling and ETL/ELT.
• ● Strong understanding of software engineering fundamentals, including API design, testing, security and CI/CD.
• ● Hands-on experience designing or prototyping LLM-powered applications, agentic workflows or retrieval-augmented generation solutions.
• ● Understanding of GenAI architecture patterns, including model integration, embeddings, vector search, tool calling, evaluation and observability.
• ● KEY COMPETENCIES
• ● Architectural Judgment: Balances build speed, cost, scalability, security and maintainability across data, software and GenAI solutions.
• ● Applied AI Judgment: Understands both the potential and limitations of GenAI and can separate valuable applications from unnecessary complexity.
• ● Hands-On Technical Leadership: Can personally validate difficult technical decisions while enabling engineering teams to take the solution into production.
• ● Client Communication: Makes complex architectural and AI decisions understandable to engineers, business stakeholders and senior leaders.
• ● Problem Solving: Brings structure to ambiguous technical challenges and takes initiative without waiting for detailed direction.
• ● Stakeholder Mentality: Treats the company's and client's goals as their own and is genuinely motivated by delivery success.
• ● Discretion & Integrity: Handles sensitive client data, technical information and AI risks with professionalism and sound judgment.
• ● Collaboration: Builds strong working relationships across clients, consultants and engineering teams.
• ● WHY THIS ROLE IS DIFFERENT
• ● Shape real GenAI and data solutions for leading life sciences companies, from initial problem definition through production delivery.
• ● Work across agentic workflows, enterprise data, applications and decision-support products-not isolated AI experiments.