Clinisys is a leading global provider of intelligent informatics solutions, offering advanced technologies and expert services that transform modern laboratory operations across diverse sectors such as healthcare, life sciences, public health, environmental testing, water quality, toxicology, and contract services. Through its platform and cloud-based solutions, Clinisys empowers over 3,000 laboratories in 34 countries to generate millions of analytical and diagnostic results, and data insights every day, supporting more efficient and effective workflows for laboratories and testing environments worldwide.
Headquartered in Tucson, Arizona, and Woking, England, Clinisys’ mission is to enhance the effectiveness of diagnostic workflows in any laboratory or testing environment and keep citizens and communities healthier and safer.
Clinisys' AI Philosophy:
Building an AI‑first organisation is central to Clinisys’ purpose and the impact we deliver. As a global provider of intelligent diagnostic informatics solutions, we build AI‑enabled, cloud‑based platforms to enhance diagnostic workflows across healthcare, life sciences, and public health. By applying intelligent technology thoughtfully and responsibly, we help laboratories and testing environments operate more effectively, generate meaningful insights at scale, and ultimately support healthier and safer communities. Operating across more than 30 countries, Clinisys expects all colleagues—regardless of role or function—to work confidently with AI‑enabled tools, apply digital and analytical thinking, and continuously adapt as technologies evolve, must drive an AI first sense of purpose and urgency.
Purpose (of the role):
As an AI Solutions Architect and a member of a dynamic and multi-functional Agile development team, you will play a pivotal role in championing AI-enabled capabilities into software development operations and commercialized product offerings. Clinisys is building a dedicated AI engineering team to transform how laboratories configure and interact with LIMS and LIS products. The AI Solutions Architect will lead the technical design and integration of agentic AI systems that simplify workflows, reduce configuration time, and align with our Microsoft-first technology strategy. This role will set the technical standard for AI development at Clinisys, guiding both the dedicated AI team and enabling full-stack engineers to contribute effectively.
Practical experience in deploying AI solutions in a Production environment is essential. Your hands-on experience and past leadership experience using GenAI tools—such as ChatGPT, Copilot, etc. and your familiarity with the latest versions of popular LLMs such as Claude or GPT-X—as well as a strong foundational understanding of other AI domains (NLP, Predictive Analytics, Computer Vision) will be instrumental in creating operational efficiencies and commercialized product features.
You will collaborate with product managers, UX designers, and data scientists to deliver and champion AI-enabled, GenAI, and agentic solutions for our customers to help drive their productivity. You will lead the process improvement initiatives within the SDLC to ensure applicable areas are AI-enabled, creating agentic workflows were needed, which can include user-story generation, code-generation, test-automation, PR automation etc.
You will design, host, and deploy AI-enabled solutions—including GenAI, Predictive Analytics, LLM, and RAG—ensuring effective integration with Clinisys applications. You are also responsible for cost-efficient cloud architecture for both internal and external solutions.
Essential Functions
- AI Architecture Design: Lead the design of AI-enabled, GenAI, LLM and RAG, solutions that integrate with LIMS/LIS platforms to support sample tracking, test result interpretation, anomaly detection, and workflow optimization.
o Architect scalable, secure, and compliant AI systems using Microsoft Azure services:
§ Azure OpenAI, Azure Machine Learning, Azure Functions, Azure Service Bus
o Design agentic workflows that allow an LLM-powered assistant to autonomously build and modify configurations within LIMS/LIS products
o Create reference implementations and reusable components for full-stack engineers to extend AI functionality
o Lead architecture reviews and technical workshops to align engineering practices across teams
o Define API contracts and integration patterns between AI services and product backends:
§ .NET (C#), RDBMS, SQL Server , Oracle, NoSQL, Azure Cosmos, MongoDB, RESTful APIs
- Data Integration & Governance: Architect secure and scalable pipelines for ingesting structured and unstructured lab data (e.g., HL7, FHIR, ASTM, DICOM) while ensuring compliance with CLIA, CAP, HIPAA, and GDPR.
- Collaboration: Partner with lab directors, pathologists, and clinical informatics teams to translate diagnostic and operational needs into AI capabilities.
o Collaborate with product engineering teams to embed AI capabilities into existing UIs and workflows
- Model Development & Deployment: Oversee the lifecycle of AI models, including training, validation, deployment, and monitoring in regulated environments.
- Automation & Efficiency: Drive intelligent automation initiatives such as auto-verification of results, smart routing of samples, and predictive maintenance of lab instruments.
- Compliance & Explainability: Ensure AI solutions meet regulatory standards and provide explainable outputs suitable for clinical environments.
- Innovation Leadership: Evaluate emerging technologies (e.g., LLMs and RAG for lab report summarization, computer vision for slide analysis) and lead proof-of-concept initiatives.
Skills needed to be successful
- Strong architectural thinking with the ability to design scalable, modular, and secure AI systems
- Deep understanding of large language models (LLMs) and RAG, agentic systems, and workflow orchestration
- Proficiency in both Python and .NET (C#) for building production-grade AI services and APIs
- Expertise in Microsoft Azure services, including Azure OpenAI, Azure ML, and Azure DevOps
- Ability to translate business and domain-specific requirements into technical solutions
- Strong communication and mentoring skills to guide engineers and influence cross-functional teams
- Familiarity with regulatory and compliance frameworks relevant to healthcare and life sciences
- Comfortable working in a fast-paced, cross-functional environment with evolving priorities
- Strong problem-solving and debugging skills.
- Excellent communication and collaboration skills.
Required Experience & Education
- Bachelor’s degree in Computer Science, Engineering, or a related technical field
- 8+ years of experience in software architecture or engineering
- 3+ years of experience designing and implementing AI/ML systems in production environments
- Demonstrated experience with Microsoft Azure cloud services and infrastructure
- Proven track record of integrating AI systems into enterprise software products
Preferred Experience & Education
- Master’s degree in Computer Science, Data Science, or a related field
- Experience in regulated industries such as healthcare, life sciences, or environmental sciences
- Familiarity with laboratory information systems (LIMS/LIS) or scientific data workflows
- Experience with embedding AI assistants or chatbots into enterprise applications
- Knowledge of prompt engineering, vector databases, and retrieval-augmented generation (RAG)
Supervisory Responsibilities
- This role does not have direct reports initially but will:
o Provide technical leadership and mentorship to AI engineers and full-stack contributors
o Influence architectural decisions across multiple engineering teams
o Help define future team structure and hiring needs as the AI initiative scales
- AI Architecture Design: Lead the design of AI/ML solutions that integrate with LIMS/LIS platforms to support sample tracking, test result interpretation, anomaly detection, and workflow optimization.
- Data Integration & Governance: Architect secure and scalable pipelines for ingesting structured and unstructured lab data (e.g., HL7, FHIR, ASTM, DICOM) while ensuring compliance with CLIA, CAP, HIPAA, and GDPR.
- Clinical Collaboration: Partner with lab directors, pathologists, and clinical informatics teams to translate diagnostic and operational needs into AI capabilities.
- Model Development & Deployment: Oversee the lifecycle of AI models, including training, validation, deployment, and monitoring in regulated environments.
- Automation & Efficiency: Drive intelligent automation initiatives such as auto-verification of results, smart routing of samples, and predictive maintenance of lab instruments.
- Compliance & Explainability: Ensure AI solutions meet regulatory standards and provide explainable outputs suitable for clinical environments.
- Innovation Leadership: Evaluate emerging technologies (e.g., LLMs for lab report summarization, computer vision for slide analysis) and lead proof-of-concept initiatives.
