AI Engineer at HapiVet AI · Hyderabad · 3 - 6 years · ₹5L - ₹35L / yr · Raised funding · Posted 9 Dec 2025
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.
Shubham Vishwakarma
Full Stack Developer - Averlon
About HapiVet AI
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Must of Skills/Experience
• System Design
• Python
• TensorFlow
• Google ADK or Lang Graph
• Lang Chain , Lang Graph
• Spark
• Agentic AI Design
• ML Ops
• MCP (client and server)
• FastAPI
• Doc Factory
• RAG
• Golang
• LLMs – Gemini, Open AI
• NLP
• Dev Assistant - AI based code - generation
(Qwen or Claude or Copilot)
• CI/CD
• Good in oral and written communication,
collaboration and be a team player
Good to have skills
• DevOps with K8
• Scripting
• Java
• REST API
• UV
• ReACT
• DocFactory
• Unix
Skill Set
Large language,Artificial Intelligence,Machine Learning
- 4–7 years of experience in software engineering/AI roles
- Strong programming skills in Python or TypeScript (Java/Go is a plus)
- Hands-on experience with LLMs, RAG pipelines, and AI frameworks
- Experience building APIs and working with distributed systems
- Familiarity with Kubernetes, Docker, and CI/CD pipelines
- Experience with cloud platforms (AWS/Azure/GCP)
Excellent communication
Job Summary/ Job Opportunity:
This is an excellent opportunity for an ideal candidate with a high level of technical proficiency and meeting the below mentioned criteria -- • Strong experience in Machine Learning, Deep Learning, Generative AI, and Large Language Models (LLMs). • Hands-on experience building and deploying production-grade solutions using Azure OpenAI, OpenAI, LangChain, LangGraph, Semantic Kernel, LlamaIndex, and Agentic AI frameworks. • Strong expertise in Python, API development, microservices, and cloud-native architectures. • Experience designing and implementing RAG solutions, vector databases, embeddings, knowledge retrieval systems, and AI copilots. • Experience with Azure cloud services, MLOps, CI/CD pipelines, monitoring, and model lifecycle management. • Strong understanding of AI governance, responsible AI, security, compliance, and model evaluation frameworks. • Ability to lead technical discussions, provide architectural recommendations, mentor team members, and interact with business stakeholde
Key Objectives and Major Responsibilities:
• Design, develop, and implement scalable AI/ML and Generative AI solutions for enterprise applications. • Lead development of intelligent applications leveraging LLMs, RAG pipelines, AI agents, and document intelligence solutions. • Collaborate with business stakeholders, architects, and product teams to translate business requirements into technical solutions. • Design and optimize data pipelines, vector search solutions, embeddings, and retrieval mechanisms. • Build and maintain REST APIs, microservices, and cloud-native AI applications. • Ensure best practices in coding standards, performance optimization, security, scalability, and maintainability. • Drive AI solution deployment using MLOps practices, CI/CD pipelines, monitoring, and observability frameworks. • Perform code reviews, mentor junior developers, and contribute to capability building within the team
Key Capabilities and Competencies:
Knowledge, Skills, Qualification and Experience
• Degree in B.Tech/M.Tech (Computer Science/IT/Data Science) or related discipline preferred, with 3–4 years of relevant experience in AI/ML, GenAI and total 5-7 years of experience. • Proficiency in Python and hands-on experience with ML libraries (scikit-learn, TensorFlow, PyTorch) and GenAI frameworks/tools. • Strong understanding of machine learning, deep learning, LLMs, prompt engineering, and techniques like RAG and fine-tuning. • Experience with data processing, embeddings, vector databases, APIs, and building scalable AI driven applications. • Good communication skills, ability to work on multiple projects, and eagerness to learn and adapt to evolving AI technologies.
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Mandatory (Experience 3) – Must have experience working with Machine Learning and Deep Learning frameworks such as PyTorch, TensorFlow, Keras, Scikit-learn, or equivalent.
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Mandatory (Experience 4) – Must have hands-on experience working on NLP, embeddings, semantic search, text classification, document understanding, recommendation systems, or similar AI/ML use cases.
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Mandatory (Experience 5) – Must have experience working with Large Language Models (LLMs) such as GPT, Llama, Mistral, Claude, Gemini, Phi, or similar foundation models.
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Mandatory (Experience 6) – Must have hands-on experience building or implementing RAG (Retrieval Augmented Generation) systems, vector search, knowledge retrieval, embeddings, chunking, indexing, or semantic retrieval solutions.
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Mandatory (Experience 7) – Must have experience working with Git, CI/CD practices, production environments, and scalable AI/ML systems.
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Mandatory (Experience 3) – Must have experience working with Machine Learning and Deep Learning frameworks such as PyTorch, TensorFlow, Keras, Scikit-learn, or equivalent.
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Mandatory (Experience 4) – Must have hands-on experience working on NLP, embeddings, semantic search, text classification, document understanding, recommendation systems, or similar AI/ML use cases.
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Mandatory (Experience 5) – Must have experience working with Large Language Models (LLMs) such as GPT, Llama, Mistral, Claude, Gemini, Phi, or similar foundation models.
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Mandatory (Experience 6) – Must have hands-on experience building or implementing RAG (Retrieval Augmented Generation) systems, vector search, knowledge retrieval, embeddings, chunking, indexing, or semantic retrieval solutions.
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Mandatory (Experience 7) – Must have experience working with Git, CI/CD practices, production environments, and scalable AI/ML systems.
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Mandatory (CTC) – The CTC breakup offered will be 75% fixed + 25% variable, as per company policy.
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Mandatory (Age) - Candidate's Age should be below 28 Years
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Mandatory (Experience 1) – Must have minimum 5+ years of hands-on experience in Data Science, Machine Learning, Applied AI, NLP, Deep Learning, or Generative AI solutions.
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Mandatory (Experience 2) – Must have strong hands-on experience in Python programming, SQL, data analysis, feature engineering, model development, and production-grade ML applications.
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Mandatory (Experience 3) – Must have experience working with Machine Learning and Deep Learning frameworks such as PyTorch, TensorFlow, Keras, Scikit-learn, or equivalent.
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Mandatory (Experience 4) – Must have hands-on experience working on NLP, embeddings, semantic search, text classification, document understanding, recommendation systems, or similar AI/ML use cases.
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Mandatory (Experience 5) – Must have experience working with Large Language Models (LLMs) such as GPT, Llama, Mistral, Claude, Gemini, Phi, or similar foundation models.
7
Mandatory (Experience 6) – Must have hands-on experience building or implementing RAG (Retrieval Augmented Generation) systems, vector search, knowledge retrieval, embeddings, chunking, indexing, or semantic retrieval solutions.
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Mandatory (Experience 7) – Must have experience working with Git, CI/CD practices, production environments, and scalable AI/ML systems.
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Mandatory (CTC) – The CTC breakup offered will be 75% fixed + 25% variable, as per company policy.
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Mandatory (Age) - Candidate's Age should be below 30 Years
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Preferred (Experience 1) – Experience with MLFlow, Kubeflow, Airflow, Prefect, Feature Stores, Model Registry, or MLOps/LLMOps frameworks.
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Experience - 4 to 6 year
Location – Ahmedabad/Pune/Indore
- Additional Job Description
Additional Job Description
Required Skills and Experience:
- Strong proficiency in Python and experience with ML/AI libraries (scikit-learn, TensorFlow, PyTorch, Hugging Face ecosystem).
- Hands-on experience with LLMs, RAG, vector databases, and retrieval pipelines.
- Practical experience deploying agentic workflows and building multi-step, tool-enabled agents.
- Experience using Garak (or similar LLM red-teaming/vulnerability scanners) to identify model weaknesses and harden deployments.
- Demonstrated experience implementing content filtering / moderation systems.
- Solid skills working with structured and unstructured data and advanced feature engineering.
- Familiarity with cloud GenAI platforms and services (Azure AI Services preferred; AWS/GCP acceptable).
- Experience building APIs/microservices; containerization (Docker), orchestration (Kubernetes).
- Strong understanding of model evaluation, performance profiling, inference cost optimization, and observability.
- Good knowledge of security, data governance, and privacy best practices for AI systems.
The recruiter has not been active on this job recently. You may apply but please expect a delayed response.
Key Responsibilities
- Design, build, and optimize scalable data pipelines for AI/ML applications.
- Develop, train, evaluate, and deploy Machine Learning and Deep Learning models.
- Build production-ready LLM applications using Retrieval-Augmented Generation (RAG), prompt engineering, and vector databases.
- Fine-tune open-source and foundation models using domain-specific datasets.
- Develop and maintain end-to-end MLOps pipelines for model deployment, monitoring, and lifecycle management.
- Perform data preprocessing, feature engineering, exploratory data analysis (EDA), and model evaluation.
- Develop APIs and AI services for production deployment.
- Collaborate with cross-functional teams to deliver scalable AI-driven solutions.
- Monitor model performance, troubleshoot production issues, and maintain technical documentation.
Required Skills
Mandatory
- 1–3 years of experience in Data Science, Data Engineering, or AI/ML development.
- Strong programming skills in Python and SQL.
- Hands-on experience with Machine Learning frameworks such as PyTorch, TensorFlow, or Scikit-learn.
- Experience building LLM-powered applications using RAG, Prompt Engineering, and Embeddings.
- Hands-on experience with LangChain, LlamaIndex, CrewAI, or n8n for LLM orchestration and AI workflow automation.
- Experience in LLM fine-tuning and working with Hugging Face models.
- Knowledge of MLOps concepts including model deployment, monitoring, versioning, and CI/CD.
- Experience with Git, REST APIs, Linux environments, and data processing libraries.
Preferred
- Experience with vector databases such as Pinecone, Chroma, Milvus, or Weaviate.
- Familiarity with Docker, Kubernetes, and MLflow.
- Exposure to Apache Spark or Airflow for data engineering workflows.
- Experience with cloud platforms (AWS, Azure, or GCP).
Primary Technology Stack
- Languages & Data Processing: Python, SQL, Pandas, NumPy, Apache Spark
- AI & Machine Learning: PyTorch, TensorFlow, Scikit-learn
- Application Frameworks: LangChain, LlamaIndex, CrewAI, n8n
- Core Methodologies: Retrieval-Augmented Generation (RAG), Model Fine-Tuning, Prompt Engineering, Embeddings
- Models & Infrastructure: OpenAI APIs, Hugging Face Ecosystem, Embedding Models
- Vector Databases: Pinecone, Chroma, Milvus, Weaviate
- Databases: PostgreSQL, MongoDB
- MLOps & DevOps: Docker, Kubernetes, MLflow, CI/CD, Git
- Cloud Platforms: AWS, Azure, GCP
Experience: 1–3 Years
Domain: Data Science | Data Engineering | Machine Learning | Generative AI | MLOps
AI based systems design and development, entire pipeline from image/ video ingest, metadata ingest, processing, encoding, transmitting.
Implementation and testing of advanced computer vision algorithms.
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Detailed analysis of results. Documentation, version control, client support, upgrades.
Strong AI Engineer / Machine Learning Engineer profiles.
2
Mandatory (Experience 1) – Must have minimum 3+ years of hands-on experience in Data Science, Machine Learning, Applied AI, NLP, Deep Learning, or Generative AI solutions.
3
Mandatory (Experience 2) – Must have strong hands-on experience in Python programming, SQL, data analysis, feature engineering, model development, and production-grade ML applications.
4
Mandatory (Experience 3) – Must have experience working with Machine Learning and Deep Learning frameworks such as PyTorch, TensorFlow, Keras, Scikit-learn, or equivalent.
5
Mandatory (Experience 4) – Must have hands-on experience working on NLP, embeddings, semantic search, text classification, document understanding, recommendation systems, or similar AI/ML use cases.
6
Mandatory (Experience 5) – Must have experience working with Large Language Models (LLMs) such as GPT, Llama, Mistral, Claude, Gemini, Phi, or similar foundation models.
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Mandatory (Experience 6) – Must have hands-on experience building or implementing RAG (Retrieval Augmented Generation) systems, vector search, knowledge retrieval, embeddings, chunking, indexing, or semantic retrieval solutions.
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Mandatory (Experience 7) – Must have experience working with Git, CI/CD practices, production environments, and scalable AI/ML systems.
9
Mandatory (CTC) – The CTC breakup offered will be 75% fixed + 25% variable, as per company policy.
10
Mandatory (Age) - Candidate's Age should be below 28 Years
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Preferred (Experience 1) – Experience with MLFlow, Kubeflow, Airflow, Prefect, Feature Stores, Model Registry, or MLOps/LLMOps frameworks.
12
Preferred (Experience 2) – Experience working with Vector Databases, Spark, PySpark, distributed ML pipelines, large-scale data processing, or real-time ML systems..
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Preferred (Experience 3) – Familiarity with Docker, Kubernetes, Azure, AWS, GCP, cloud-native AI deployments, and scalable ML architecture.
14
Preferred (Company) – Candidates from AI-first startups, Fintech, Banking, Lending, Fraud Analytics, Risk Analytics, Product Companies, SaaS organizations, or data-driven technology companies
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Mandatory ( Pedigree) - B.TECH / M.TECH from Tier 1 Colleges (IIT's, NIT's, BITS) are Considered.
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Full Stack Developer - Averlon