Senior Software Engineer AI

GTM Tech team at Intuit is looking for 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 for insights, recommendations from various data sources. You'll take a lead at building architecture for LLM apps that can automate marketing workflows, data retrieval to possibly fine tuning LLMs for marketing needs. You will partner with AI/data teams at Intuit and work closely with the platform engineers to identify/build durable frameworks/components that enable platform engineers to build AI based assists, integrate model based automations in their tools. 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 that fit use-cases by working with the consuming teams and dependencies. Being able to navigate through ambiguity, lead with clarity and PoC Rapid prototyping, experimentation and iterations to build high accuracy/performant frameworks/models. Self organized, explore new shifts in GenAI and look at possible application/improvements in the existing use cases. Being a team player to build the team and strengthen building with AI across the organization. Being opinionated on data to make it ready for training and inferences. BS, MS, or PhD degree in Computer Science or related field, or equivalent practical experience. 5+ years’ experience developing web, software, or mobile applications. Knowledgeable with Data Tools and frameworks (i.e. Python, PyTorch, Numpy, Pandas, TensorFlow, R, Spark). Knowledge of machine learning (i.e. classification, regression, clustering, neural networks) and MLOps. Understand and apply machine learning principles (training, weights, validation, testing, error, cost) optimizing for accuracy 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) In-depth knowledge of Transformer, Encoder, Embeddings at scale. Understanding of LLM, LangChain, CustomGPTs, Prompt Management, and ability to fine-tune base models to build efficient production-grade LLM apps. Nice to have: Knowledge of data cleaning, streaming, transformations at scale, storage and ingestion pipelines.

#J-18808-Ljbffr