Key Responsibilities:
- Design AI-driven solutions for core veterinary workflows, such as patient triaging, diagnostics support, treatment plan suggestions, appointment scheduling, and client communications.
- Develop AI assistants and multi-agent systems to automate routine tasks like SOAP note summarization, clinical documentation (Medical Records), prescription and other reminders, and client follow-ups.
- Implement RAG pipelines leveraging veterinary knowledge bases, clinical case data, Case Summaries and standard care protocols.
- Integrate LLMs into practice management modules for intelligent querying, FAQ automation, and veterinary clinical knowledge support.
- develop and deploy AI services using Azure AI Services, Azure OpenAI, and integrate with Hapivet.ai
- Ensure secure, compliant, and scalable deployment of AI/ML models in line with veterinary data privacy standards and healthcare regulations.
- Collaborate with veterinarians, product managers, and software engineers to ensure AI solutions are clinically relevant, user-friendly, and impactful.
Required Skillset:
Machine Learning & Deep Learning for Healthcare
- Strong foundation in supervised/unsupervised learning, anomaly detection, and predictive analytics applicable to veterinary clinical data.
- Experience with CNNs (for imaging), RNNs/LSTMs (for sequential data like patient histories) and Transformers for natural language tasks.
- Proficiency with TensorFlow, PyTorch, and Hugging Face.
- Good to have understanding on GANs (medical imaging, data privacy-safe synthetic data, or image-based diagnostics)
LLMs & NLP for Veterinary Applications
- Deep understanding of transformer models (GPT, BERT, LLaMA) applied in medical/veterinary text summarization and knowledge extraction.
- Fine-tuning LLMs with techniques with PEFT, LoRA, QLoRA for domain-specific tasks.
- Expertise in Prompt Engineering and Chain of Thought (CoT) design for veterinary use cases.
- RAG pipeline development with veterinary case databases using Pinecone or Azure AI Search.
AI Agent Workflows & Orchestration
- Multi-agent coordination and AI workflow orchestration with LangChain, LangGraph, and Microsoft Autogen SDK.
- Experience with context management using Model Context Protocol (MCP) in clinical task flows.
Cloud AI Deployment & Engineering
- Experience with Azure AI, model serving (Triton, TensorFlow Serving, TorchServe).
- CI/CD for AI models, cloud security, and scalable API integration.
Programming & Data Engineering
- Advanced proficiency in Python, R
- working knowledge of TypeScript.
- Veterinary data processing experience—handling EMRs, patient histories, and diagnostic reports
- Data cleaning, transformation, and ensuring data quality for clinical applications.
Preferred Experience:
- AI/ML deployment in veterinary practice management systems or healthcare applications.
- Understanding of veterinary compliance, data sensitivity, and client confidentiality (e.g., pet health records, veterinary licensing).
- Exposure to veterinary-specific AI applications, such as diagnostic imaging analysis, clinical decision support systems, or client interaction bots.
- Familiarity with DeepSpeed, Megatron-LM, and scaling techniques for LLMs.
Soft Skills & Domain Understanding:
- “Passion for improving pet care and veterinary services through technology”.
- Strong communication skills for collaborating with veterinary professionals.
- Ability to translate clinical workflows into AI-enabled solutions.
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