Staff Software Engineer AI

Job Overview

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 take a lead at designing/building architecture for 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 identify/build durable frameworks/components that enable platform engineers to build AI based assists, integrate model based automations in their tools.

We are looking for

● Leads who can drive architecture/design, implementation and deployment of models, LLM apps.

● Passionate engineers and applied scientists with experience in understanding

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.
• Being able to navigate through ambiguity, lead with clarity and PoC
• Rapid prototyping, experimentation and iterations to build/own high accuracy/performant frameworks/models.
• Self organized, explore new shifts in GenAI/AI 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/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 8+ years of experience in building/designing AI/ML applications. Atleast 1 year of experience working as a lead for a team with AI/ML engineers.
• Well versed in building with Python, PyTorch, Numpy, Pandas, TensorFlow
•  Machine learning fundamentals for supervised, unsupervised & reinforcement learning (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. Should establish and own metrics/optimization.
• 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.
• Nice to have: Knowledge of data cleaning, streaming, transformations at scale,storage and ingestion pipelines.