AI Engineer

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