AI Engineer

Summary

Join Emburse as an AI Engineer and build the future of automated invoice and expense management. You will design, develop, and deploy AI solutions using various approaches, including foundation models, agents, deep learning, and other machine learning techniques. Collaborate with cross-functional teams to build and train machine learning models, develop algorithms, and integrate AI systems into existing software. Identify opportunities for AI applications and drive innovation across the enterprise. Stay updated on advancements in AI and explore new tools for fine-tuning and deploying open-source foundation models. Leverage agents to retrieve knowledge and make autonomous inferences to handle user inquiries. This role is crucial for Emburse's competitiveness in a rapidly evolving technological landscape.

Requirements

• BS in Statistics, Mathematics, Computer Science or another quantitative field with at least 6 years experience manipulating data sets and building GLM/regression models, ensemble decision trees and neural networks
• The ideal candidate will have demonstrated hands-on experience with foundation models, GenAI and agents over the last 1-2 years
• Strong problem solving skills with an emphasis on product development
• 6+ years of experience developing data science products
• Strong experience using and optimizing common python machine and deep learning libraries such as Scikit learn, PyTorch, TensorFlow, Keras, MXNet and Spark MLlib
• Experience using statistical computer languages (Python, R, Scala, SQL, etc.) to manipulate data and draw insights from large data sets
• Hands-on generative AI development Experience using foundation models (LLMs, VLMs)
• Experience with model fine tuning of open source foundation models with proprietary data
• Experience leveraging AI metrics for monitoring and value tracking
• Knowledge of AI Agent frameworks with recent hands-on experience building an AI agent able to autonomously use data stores, tools and other AI models to solve inquiries
• Deep knowledge of data science concepts and related product development lifecycle
• Experience using machine learning libraries such as TensorFlow, Keras, SparkML etc
• Working knowledge of machine learning tuning optimization procedures
• Experience working with and creating data architectures
• Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications
• Excellent written and verbal communication skills for coordinating across teams
• A drive to learn and master new technologies and techniques

Responsibilities

• Able to translate product design requirements into pipeline of heuristic and stochastic algorithms
• Understand exploratory data analysis for product feasibility studies and ground truth testing
• Execute SQL queries and/or python scripts to manipulate, analyze and visualize data
• Able to implement explainable AI solutions and rationalize model inferences
• Follows SDLC processes, Adopt agile-based processes/meetings and peer code-reviews
• Works with Machine learning engineer/architect to deploy data products into production
• Follows and understands legal data use restrictions
• Contributes to algorithm library development and design for ML, NLP and XAI
• Delivers product pipelines for deployment to production
• Builds applications that integrate third party and self-hosted foundation models
• Fine tunes open source foundation models (LLMs, VLMs) with proprietary data
• Develops autonomous AI inference and tool use orchestration using ReAct AI agents
• Provides root cause analysis for machine learning model inference
• Completes data analysis or processing tasks as directed
• Documents data product end to end design and development
• Data annotation, labeling and other related data generation activities
• Provides thought leadership for rest of team and seeks out opportunities to mentor more junior team members
• Presents and holds data product updates and trainings
• Updates team on data product performance

Preferred Qualifications

• Graduate degree preferred
• Experience with big data analytical frameworks such as Spark/PySpark
• Experience analyzing data from 3rd party providers: Google Knowledge Graph, Wikidata, etc
• Experience visualizing/presenting data for stakeholders using: Looker, PowerBI, Tableau, etc

Benefits

• Competitive pay
• Flexible work
• An inclusive, collaborative environment that supports your success