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.

SKILLS, QUALIFICATIONS AND EXPERIENCE

• 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.

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.
• Remain technically hands-on while operating at the solution, client and delivery level.
• Help define Lynx’s GenAI architecture, reusable patterns and technical standards as the practice grows.
• Work directly with senior client stakeholders and Lynx leadership, with meaningful autonomy over technical decisions.
• Join a collaborative, global team with high ownership, a flat hierarchy and diverse technical challenges.