Job Description

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.

Purpose (of the role):

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As an AI Software Engineer and a member of a dynamic and multi-functional Agile development team, you will play a pivotal role in embedding AI-enabled capabilities into our core applications, software development operations, transforming user experiences and automating complex workflows across laboratory and clinical environments. To excel in this role, you must demonstrate a genuine passion for quality software, dedication to customer happiness, and the ability to work effectively in a matrix organization.


Practical experience in deploying AI solutions in a Production environment is essential. Your hands-on 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 AI-enabled, GenAI, and agentic solutions for our customers to help drive their productivity. You will collaborate with process stakeholders to ensure processes within the SDLC are AI-enabled, creating agentic workflows were needed, which can include user-story generation, code-generation, test-automation, PR automation etc.


You will own the development, deployment, and integration of AI services, ensuring they meet performance, security, and compliance standards. Your work will directly influence the scalability, innovation, and intelligence of Clinisys' next-generation software solutions.

Essential Functions

Design and implement AI features across the SDLC and Clinisys products to support agentic and generative workflows. Develop and integrate LLM-powered capabilities such as intelligent assistants, natural language query builders, and contextual help systems. Fine-tune and implement large language models (LLMs), as well as Retrieval-Augmented Generation (RAG) systems (e.g., Claude, GPT-X), to address domain-specific requirements in laboratory and clinical workflows. Monitor model performance post-deployment and implement retraining strategies to maintain accuracy and relevance. Apply prompt engineering strategies to optimize LLM responses and user interactions. Ensure secure handling of AI inputs/outputs, including prompt data, embeddings, and model responses, in alignment with global data privacy regulations. Implement observability practices including logging, tracing, and monitoring for AI services. Build full-stack solutions using object-oriented programming, preferably with: + Front-End: HTML, CSS, React, Angular, JavaScript
+ Back-End: C# .NET, MS Entity Framework
+ Data: Oracle, MSSQL, Azure Cosmos, MongoDB
Scaffold and maintain APIs using controller-service-repository or similar architectural patterns. Deploy solutions across on-prem, cloud, and cloud-native environments. Ensure robust testing coverage including unit, integration, and performance tests. Support DevOps automation for AI-enabled services using CI/CD pipelines. Collaborate with UX and product teams to deliver intelligent, user-centric experiences. Troubleshoot and resolve integration and deployment challenges. Ensure compliance with relevant standards and regulations, including ISO 13485, ISO 9001, Section 508, WCAG, and 21 CFR Part 11. Document technical specifications, integration workflows, and architectural decisions. Mentor junior developers and promote best practices in AI integration. Perform other duties as assigned.

Skills needed to be successful

Experience with LLM APIs (OpenAI, Azure OpenAI, Anthropic). Demonstrates a comprehensive knowledge of RAG configuration and the integration of large language model outputs with targeted domain-specific data. Strong understanding of prompt engineering, fine-tuning, and embedding techniques. Familiarity with popular models such as GPT-X, Claude, Gemini, Grok, etc. Familiarity with transformer architectures and NLP pipelines. Ability to assess and mitigate risks related to bias, hallucination, and data privacy in LLMs. Contribute to AI governance initiatives and responsible AI practices. Proficiency in: + Front-End: HTML, CSS, React (preferred), Angular, JavaScript
+ Back-End: C# .NET, MS Entity Framework
+ Data: Oracle, MSSQL, Azure Cosmos, MongoDB
+ Containerization & Orchestration: Docker, Kubernetes
+ DevOps & CI/CD: Azure DevOps (preferred), GitHub Actions, Jenkins
+ Cloud Platforms: Azure (preferred), AWS, GCP
+ AI/ML Tooling: Azure OpenAI Service, Azure Machine Learning, Azure AI Studio, Azure Cognitive Services, Azure AI Search
+ Security & Identity: OAuth2, OpenID Connect, JWT, Azure AD
+ Messaging & Streaming: Azure Event Grid (preferred), Kafka, RabbitMQ
+ Monitoring & Observability: Azure Monitor (preferred), Prometheus, ELK Stack, Grafana
Experience deploying to cloud-native environments using containerization. Familiarity with AI/ML frameworks and model lifecycle management. Strong debugging, analytical, and problem-solving skills. Excellent verbal and written communication. Collaborative mindset with the ability to mentor and lead by example.

Required Experience & Education

Bachelor's degree in Software Engineering, Computer Science, or related field. 5+ years of full-stack software development experience. 3+ years of experience integrating AI into software products. Deep understanding of agile software development methodologies.

Preferred Experience & Education

Master's degree in Computer Science or related discipline. Experience with scientific data software, medical devices, healthcare, or laboratory systems. Familiarity with LIS/LIMS platforms. Knowledge of regulatory frameworks including HIPAA, CLIA, GDPR, and accessibility standards. Experience with healthcare interoperability protocols (HL7, FHIR, ASTM). Experience working with globally distributed teams. Relevant certifications such as: + Microsoft Certified: Azure AI Engineer Associate
+ Google Professional Machine Learning Engineer

Supervisory Responsibilities

* None