
Senior AI Engineer for Veterinary Solutions
1 week ago
- Key Responsibilities:
- We are seeking a skilled AI engineer to design and implement AI-driven solutions for core veterinary workflows. This includes developing AI assistants, multi-agent systems, and RAG pipelines leveraging veterinary knowledge bases and clinical case data.
- The ideal candidate will have experience with Azure AI Services, Azure OpenAI, and Hapivet.ai, as well as proficiency in TensorFlow, PyTorch, and Hugging Face.
- They will be responsible for ensuring secure, compliant, and scalable deployment of AI/ML models in line with veterinary data privacy standards and healthcare regulations.
- Collaboration with veterinarians, product managers, and software engineers is essential to ensure AI solutions are clinically relevant, user-friendly, and impactful.
- Required Skillset:
- Machine Learning & Deep Learning for Healthcare:
- A strong foundation in supervised/unsupervised learning, anomaly detection, and predictive analytics applicable to veterinary clinical data is required.
- Experience with CNNs (for imaging), RNNs/LSTMs (for sequential data like patient histories), and Transformers for natural language tasks is highly desirable.
- Proficiency with TensorFlow, PyTorch, and Hugging Face is necessary.
- A good understanding of GANs (medical imaging, data privacy-safe synthetic data, or image-based diagnostics) is also beneficial.
- LLMs & NLP for Veterinary Applications:
- A deep understanding of transformer models (GPT, BERT, LLaMA) applied in medical/veterinary text summarization and knowledge extraction is essential.
- Experience with fine-tuning LLMs using techniques such as PEFT, LoRA, QLoRA for domain-specific tasks is highly desirable.
- Expertise in Prompt Engineering and Chain of Thought (CoT) design for veterinary use cases is necessary.
- RAG pipeline development with veterinary case databases using Pinecone or Azure AI Search is also beneficial.
- AI Agent Workflows & Orchestration:
- Multi-agent coordination and AI workflow orchestration with LangChain, LangGraph, and Microsoft Autogen SDK is highly desirable.
- Experience with context management using Model Context Protocol (MCP) in clinical task flows is necessary.
- Cloud AI Deployment & Engineering:
- Experience with Azure AI, model serving (Triton, TensorFlow Serving, TorchServe), and CI/CD for AI models is essential.
- Cloud security and scalable API integration are also necessary.
- Programming & Data Engineering:
- Advanced proficiency in Python, R, and Agentic AI is necessary.
- Working knowledge of TypeScript is also beneficial.
- Veterinary data processing experience-handling EMRs, patient histories, and diagnostic reports is essential.
- Data cleaning, transformation, and ensuring data quality for clinical applications is also necessary.
- Preferred Experience:
- AI/ML deployment in veterinary practice management systems or healthcare applications is highly desirable.
- Understanding of veterinary compliance, data sensitivity, and client confidentiality is essential.
- Exposure to veterinary-specific AI applications, such as diagnostic imaging analysis, clinical decision support systems, or client interaction bots is beneficial.
- Familiarity with DeepSpeed, Megatron-LM, and scaling techniques for LLMs is also desirable.
- Soft Skills & Domain Understanding:
- Machine Learning & Deep Learning for Healthcare:
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