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.
  
  

Skills:- Generative AI, Natural Language Processing (NLP), Retrieval Augmented Generation (RAG), Large Language Models (LLM), PyTorch and TensorFlow