Solution Architect - GenAI, Data & Applications (US)

We need to output HTML fragment with sections: About the role? The posting includes summary statement, key responsibilities, skills/qualifications/experience, key competencies, why this role is different. We need headings:

for section headings like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We need to map content accordingly. We have summary statement: could be "About the role". Use

About the role

then

with summary. Responsibilities: key responsibilities bullet list. Use

Responsibilities

then

• each bullet. Requirements/Qualifications/Skills: The posting has "SKILLS, QUALIFICATIONS AND EXPERIENCE" and "KEY COMPETENCIES". We need to decide sections. Could combine qualifications and skills into one section "Requirements" or separate. The instruction: use headings for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Skills). We can include multiple if content exists. We have qualifications (education, years experience, etc.) and skills (technical proficiencies). Also key competencies (soft skills). Could make sections: Requirements (education + experience), Skills (technical skills), Qualifications maybe redundant. But we can follow:

  Requirements

  for education and experience;

  Skills

  for technical proficiencies;

  Key Competencies

  for competencies. However "Key Competencies" not in list but we can still use heading? The instruction says use headings for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). It gives examples but not exclusive. Probably we should stick to those headings only. So we need to map content to those headings. We have About the role, Responsibilities, Requirements, Qualifications, Skills. We don't have Benefits, Pay, Schedule. So we omit those. Thus we need to decide what goes under Requirements vs Qualifications vs Skills. Could put education+experience under Requirements, technical proficiencies under Skills, and maybe key competencies under Qualifications? Or Qualifications could be education+experience, Skills could be technical proficiencies, and Requirements could be key competencies? But better: Requirements = education + experience; Qualifications = maybe none? But we have "SKILLS, QUALIFICATIONS AND EXPERIENCE" block includes both qualifications and skills. Could split: Qualifications = education + experience; Skills = technical proficiencies; Key competencies maybe go under Qualifications? But we already used Qualifications for education+experience. Could put key competencies under Skills? Not ideal. Maybe we can have three sections: Requirements (education+experience), Skills (technical proficiencies), and Qualifications (key competencies). That seems plausible. Let's extract content. 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." Responsibilities bullet list: from "KEY RESPONSIBILITIES" section. Need to capture each bullet. List: - 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. - Partner with data engineers, software engineers and AI engineers to resolve ambiguous technical problems and establish a tested foundation for delivery. - Create reference architectures, reusable components and technical standards that can be applied across engagements. - Contribute to internal tooling, accelerators and knowledge-sharing that raise the technical bar across the practice. We need to split into list items. Some lines have multiple sentences but we can keep as one
• . Now Skills, Qualifications and Experience block: "Bachelor’s degree in Computer Science, Engineering or a related field, or equivalent practical experience. 5–8+ 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. Experience integrating AI applications with enterprise data sources, applications and workflows. Experience with AWS, Azure or GCP. Experience working in a consulting or client-facing environment, including leading discovery conversations, presenting technical trade-offs and managing ambiguity. Experience in life sciences, healthcare or another regulated industry is a strong advantage." We can split: Requirements: education + years experience? Or Qualifications: education + experience? Let's assign: Qualifications: Bachelor’s degree ... or equivalent practical experience. 5–8+ 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. Skills: 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. Experience integrating AI applications with enterprise data sources, applications and workflows. Experience with AWS, Azure or GCP. Experience working in a consulting or client-facing environment, including leading discovery conversations, presenting technical trade-offs and managing ambiguity. Experience in life sciences, healthcare or another regulated industry is a strong advantage. Key Competencies block: "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." We can put under Qualifications? But we already used Qualifications for education+experience. Could put under Skills? Might be okay to have a separate section "Qualifications" for key competencies. But we already used Qualifications for education+experience. Maybe we rename: Requirements = education+experience, Skills = technical skills, Qualifications = key competencies. That matches typical: Requirements (what you need), Skills (technical abilities), Qualifications (soft/competencies). Let's do that. Thus:

  About the role

  summary

  Responsibilities

  ...

  Requirements

  education + years experience + track record

  Skills

  technical proficiencies list

  Qualifications

  key competencies list
  We must not repeat job title as heading. No metadata. Now produce HTML fragment. Make sure each
• contains the text exactly as in posting, preserving details. Ensure no extra commentary. Let's craft. Responsibilities list items: need to split each bullet as separate
• . We'll copy sentences. I'll write them:
• 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.
• 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.
• 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.
• Partner with data engineers, software engineers and AI engineers to resolve ambiguous technical problems and