AI Software Engineer

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:

o Front-End: HTML, CSS, React, Angular, JavaScript

o Back-End: C# .NET, MS Entity Framework

o 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:

o Front-End: HTML, CSS, React (preferred), Angular, JavaScript

o Back-End: C# .NET, MS Entity Framework

o Data: Oracle, MSSQL, Azure Cosmos, MongoDB

o Containerization & Orchestration: Docker, Kubernetes

o DevOps & CI/CD: Azure DevOps (preferred), GitHub Actions, Jenkins

o Cloud Platforms: Azure (preferred), AWS, GCP

o AI/ML Tooling: Azure OpenAI Service, Azure Machine Learning, Azure AI Studio, Azure Cognitive Services, Azure AI Search

o Security & Identity: OAuth2, OpenID Connect, JWT, Azure AD

o Messaging & Streaming: Azure Event Grid (preferred), Kafka, RabbitMQ

o 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.

• 3+ years of full-stack software development experience.

• 3+ years of experience integrating AI into software products.

• Deep understanding of agile software development methodologies.

 

Prefe