Software Engineer 2 AI

Customer Growth and Engagement team at Intuit is looking for AI/ML Engineers to transform Intuit's Marketing Platforms with AI by automating/assisting in key workflows. In this role, you will partner to build ML models/pipelines for insights, recommendations, entity recognition from various data sources. You'll be part of the team implementing AI native apps that can automate marketing workflows, data retrieval to possibly fine tuning LLMs for marketing needs/data. You will partner with AI/data teams at Intuit and work closely with the platform engineers to build durable frameworks/components that enable platform engineers to build AI based assists, integrate model based automations in their tools. We are looking for passionate engineers and applied scientists with experience in understanding data, building models and optimizing for accuracy, and integrations in enterprise applications.

Responsibilities: • Responsible for design of common components/frameworks/models that assist in building AI native apps, Fullstack LLM apps. • Own end to end development of frameworks/models/ ML pipelines that fit use cases by working with the consuming teams and dependencies. • Self organized, explore new shifts in GenAI/AI and own possible application/improvements in the existing use cases. • Being opinionated on data/data security to make it ready for training and inferences.

Qualifications: • BS, MS, or PhD degree in Computer Science or related field, or equivalent practical experience with 3+ years of experience in building AI/ML applications. • Well versed in building with Python, PyTorch, Numpy, Pandas, TensorFlow • Machine learning fundamentals (i.e. classification, regression, clustering, neural networks) and experience in building production grade models with precision/recall. • Understand and apply machine learning principles (training, weights, validation, testing, error, cost) optimizing for accuracy/feedback. • Computer science fundamentals: data structures, algorithms, performance complexity, and implications of computer architecture on software performance (e.g., I/O and memory tuning). Experience with integrating applications and platforms with cloud technologies (e.g: AWS Sagemaker) • Understanding of LLM, LangChain, CustomGPTs, Prompt Management, and ability to fine-tune base models to build efficient production-grade LLM apps, In-depth knowledge of Transformer, Encoder, Embedding Models at scale.