The Role
Design, build, and deploy cloud-native AI applications using LLMs, RAG, computer vision/OCR, and agentic AI. Develop backend services, data pipelines, and MLOps workflows, train and optimize models, and collaborate with cross-functional teams to deliver production-grade AI solutions and documentation.
Summary Generated by Built In
Work Flexibility: HybridWhat You Will Do
- Design, develop, and deploy AI-powered applications leveraging Generative AI, Large Language Models (LLMs), Computer Vision, Machine Learning, and Agentic AI technologies.
- Build scalable backend services, APIs, and workflow orchestration components for AI solutions.
- Design and implement Retrieval-Augmented Generation (RAG) pipelines, intelligent assistants, and workflow automation solutions.
- Develop Computer Vision and OCR solutions for image analysis, document digitization, information extraction, and automation workflows.
- Train, optimize, evaluate, and deploy machine learning and deep learning models for production use.
- Build data pipelines supporting AI model training, validation, and inference workflows.
- Develop cloud-native AI applications and deploy solutions on Azure and other cloud platforms.
- Implement MLOps best practices including model versioning, monitoring, CI/CD, and deployment automation.
- Collaborate with cross-functional teams to translate business and clinical requirements into scalable AI solutions.
- Create technical documentation, architecture diagrams, validation reports, and deployment artifacts.
- Stay current with advancements in AI, Computer Vision, LLMs, and Agentic AI.
- Bachelor's Degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, Electrical Engineering, or a related field with 3+ years of relevant industry experience; OR
- Master's Degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, Electrical Engineering, or a related field.
- Strong programming skills in Python.
- Experience with machine learning and deep learning frameworks such as PyTorch or TensorFlow.
- Experience developing APIs, backend services, and distributed applications.
- Familiarity with Computer Vision techniques including OCR, image classification, segmentation, and object detection.
- Understanding of LLMs, Prompt Engineering, Retrieval-Augmented Generation (RAG), and Agentic AI concepts.
- Experience working with structured and unstructured data and building data pipelines.
- Experience with cloud platforms such as Azure, AWS, or GCP.
- Knowledge of databases, version control systems, CI/CD pipelines, and software development best practices.
- Familiarity with Docker, Kubernetes, and MLOps concepts.
- Experience with Azure AI Services, Azure OpenAI, Azure Machine Learning, or similar AI platforms.
- Experience with LangChain, LangGraph, Semantic Kernel, AutoGen, or equivalent AI frameworks.
- Experience building production-grade GenAI, RAG, OCR, or intelligent document processing solutions.
- Experience with MLflow, monitoring tools, and model lifecycle management.
- Exposure to healthcare, medical imaging, or regulated environments.
- Experience creating technical architecture documentation and contributing to solution design discussions.
Skills Required
- Bachelor's degree in Computer Science, AI, ML, Data Science, Software Engineering, EE with 3+ years relevant experience OR Master's degree in related field
- Strong programming skills in Python
- Experience with machine learning/deep learning frameworks such as PyTorch or TensorFlow
- Experience developing APIs, backend services, and distributed applications
- Familiarity with Computer Vision techniques including OCR, image classification, segmentation, and object detection
- Understanding of LLMs, Prompt Engineering, Retrieval-Augmented Generation (RAG), and Agentic AI concepts
- Experience working with structured and unstructured data and building data pipelines
- Experience with cloud platforms such as Azure, AWS, or GCP
- Knowledge of databases, version control systems, CI/CD pipelines, and software development best practices
- Familiarity with Docker, Kubernetes, and MLOps concepts
- Experience with Azure AI Services, Azure OpenAI, or Azure Machine Learning
- Experience with LangChain, LangGraph, Semantic Kernel, AutoGen, or equivalent AI frameworks
- Experience building production-grade GenAI, RAG, OCR, or intelligent document processing solutions
- Experience with MLflow, monitoring tools, and model lifecycle management
- Exposure to healthcare, medical imaging, or regulated environments
- Experience creating technical architecture documentation and contributing to solution design
Inari Medical Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Inari Medical and has not been reviewed or approved by Inari Medical.
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Fair & Transparent Compensation — Pay is considered competitive for many roles, with employees described as 'extremely happy' with total compensation including pay, stock, equity, and benefits. Feedback suggests above‑average salary levels across departments contribute to strong pay sentiment.
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Healthcare Strength — Benefits include comprehensive medical, dental, and vision coverage, with 100% employer‑paid premiums for employees cited in multiple descriptions. Additional options like critical illness, accident, hospital indemnity, and wellness programs reinforce the healthcare offering.
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Leave & Time Off Breadth — Time off is characterized by unlimited PTO, cited as a top benefit by employees. Paid holidays and flexible hours are also referenced, expanding the range of time‑off options.
Inari Medical Insights
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The Company
What We Do
Inari Medical, Inc. is a commercial-stage medical device company focused on developing products to treat and transform the lives of patients suffering from venous diseases.
