Artificial Intelligence Engineer

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 experiencehandling 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.