Machine Learning Engineer

2+ years of experience in developing AI solutions preferably with LLMs
5+ years of experience in Full Stack software development
5+ years of experience in Python
3+ years of experience in Javascript/Typescript, Node.js
Familiar with MLOps/deployment best practices (Sagemaker experience is a plus)
Familiarity with vector databases and text embeddings
Proven ability to manage/deploy machine learning models
Experience with AI/ML frameworks such as TensorFlow, PyTorch, numpy or scikit-learn
Strong communication and technical writing skills.
Familiarity with unit testing, debugging, profiling and performance monitoring in AWS environment.

Build and deploy machine learning models using Python (e.g., PyTorch) on platforms like AWS SageMaker
Develop backend and client-side code to integrate with cloud-based LLM APIs (e.g., Bedrock, ChatGPT APIs).
Manage monitoring (Wandb.ai, MLFlow, etc), deployment, and assist with hyperparameter tuning
Implement your own ML endpoints in our various React/NodeJS applications
Collaborate with data scientists and cross-functional teams to deliver impactful and practical AI solutions
Work in tandem with the quality engineering team to ship high-availability software
Build and maintain automated unit tests: unit, integration and UI
Participate in PR reviews and ensure proper implementation of ML tooling across our stack
Communicate effectively with engineers, product managers, customers, partners, and other leaders.
Stay up-to-date on the latest AI technologies and trends to help other areas of the company find leverage in new advancements.

Prior experience with Node.js, building features using REST and/or GraphQL APIs using Apollo preferred
Prior knowledge or ability to quickly learn developing in a MLOps environment (MLflow or similar) preferred
Ability to work a hybrid schedule – Monday/Friday WFH, Tuesday – Thursday, in office.