AI Senior Software Engineer

• 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