Job Description

• Bengaluru, Karnataka, India

Overview

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.

What you'll bring

• BS, MS, or PhD degree in Computer Science or related field, or equivalent practical experience.
• 8+ years experience designing and developing software 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.

How you will lead

• 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.