AI/ML Full Stack Developer

Nutanix • San Jose, CA

6d • Hybrid

About The Position

Are you an AI/ML engineer passionate about building intelligent systems from the ground up? Join the SaaS Engineering team at Nutanix to design, develop, and deploy production-scale machine learning solutions for our dynamic education platform serving employees, customers, and partners. You'll architect and optimize neural recommendation systems, build advanced NLP pipelines for semantic search, develop conversational AI agents using LLMs, and implement RAG frameworks. Your expertise in model training, fine-tuning, feature engineering, and MLOps will drive innovation as you work with cutting-edge frameworks and deploy models that power real-time intelligent experiences at scale. At Nutanix, you'll join the SaaS Engineering team's AI/ML division, driving innovation in our learning management system, Nutanix University. Our team is geographically distributed across India, San Jose, CA, and Durham, NC, bringing together machine learning engineers, data scientists, and MLOps specialists who collaborate on building production ML systems. We operate in a fast-paced environment where we ship models iteratively using Agile sprints, enabling rapid experimentation, model retraining, and continuous deployment of AI features. You'll work directly with distributed training infrastructure, experiment tracking platforms, and vector databases while building end-to-end ML pipelines from data ingestion to model serving. The team maintains a strong culture of knowledge sharing around emerging research, model architectures, and optimization techniques. You will report to the Director of Engineering, who champions ML innovation and provides technical mentorship to help you grow as an ML engineer. Our hybrid work model requires three days in office, facilitating collaborative model debugging sessions, architecture reviews, and hands-on pair programming while maintaining flexibility for focused deep work on complex ML problems.

Requirements

• Bachelor's degree in Computer Science, Machine Learning, Data Science, or related technical field with solid foundation in Python and familiarity with ML frameworks (PyTorch, TensorFlow, scikit-learn, Hugging Face Transformers).
• Experience or strong interest in building ML systems with understanding of model training, fine-tuning, and deployment concepts.
• Familiarity with modern AI/ML approaches including working with LLMs, prompt engineering, RAG systems using vector databases and embeddings, or agentic AI frameworks like LangGraph, LangChain, or AutoGen.
• Understanding of NLP concepts, semantic search, and retrieval strategies.
• Ability to work in Agile environments with willingness to learn, experiment, iterate on models, and take ownership of assigned ML projects and features.
• Strong communication skills, eagerness to learn from senior team members, and enthusiasm for contributing ideas, staying current with ML research, and growing as an AI/ML engineer.

Nice To Haves

• Exposure to MLOps practices like experiment tracking, model versioning, or containerization is a plus.
• Experience with GPU infrastructure or optimizing model serving is beneficial but not required.

Responsibilities

• Participate in ML sprint planning, including model experimentation roadmaps, RAG pipeline optimization, agentic workflow design, feature engineering discussions, and training pipeline estimations.
• Design, develop, and deploy machine learning models, RAG systems with vector databases and embedding models, and autonomous AI agents with tool calling capabilities, ensuring scalability, latency optimization, and alignment with business objectives.
• Conduct peer reviews of ML code, RAG retrieval strategies, agent framework implementations, model architectures, and experiment results, contributing to team-wide evaluation of semantic search quality, agent performance, and model benchmarking.
• Mentor junior ML engineers on best practices in model development, RAG architecture patterns, building multi-agent systems, prompt engineering, hyperparameter tuning, data preprocessing, and production ML systems.
• Monitor deployed models, RAG pipelines, and agentic workflows in production, manage embedding model updates, optimize retrieval performance, debug agent behavior, handle model drift detection, and maintain MLOps infrastructure for continuous delivery.
• Collaborate effectively with distributed ML teams across time zones, coordinating on shared vector stores, agent orchestration frameworks, model serving infrastructure, and cross-functional AI initiatives.
• Stay current with latest ML research, RAG optimization techniques, agentic AI frameworks like LangGraph and AutoGen, emerging model architectures, fine-tuning techniques, and GenAI advancements, bringing innovative approaches to team discussions and technical implementations.
• Document model architectures, RAG system designs, agent workflows, retrieval strategies, training procedures, feature specifications, and deployment processes through model cards, experiment tracking logs, architecture diagrams, and comprehensive ML documentation to enable reproducibility and knowledge transfer.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 5,001-10,000 employees