Principal Architect - Data Science

United Airlines - Chicago, IL

Full-time - Senior

Chicago, IL

Air Transportation

About the position

There's never been a more exciting time to join United Airlines. We're on a path towards becoming the best airline in the history of aviation. Our shared purpose - Connecting People, Uniting the World - is about more than getting people from one place to another. It also means that as a global company that operates in hundreds of locations around the world with millions of customers and tens of thousands of employees, we have a unique responsibility to uplift and provide opportunities in the places where we work, live and fly, and we can only do that with a truly diverse and inclusive workforce. And we're growing - in the years ahead, we'll hire tens of thousands of people across every area of the airline. Our careers include a competitive benefits package aimed at keeping you happy, healthy and well-traveled. From employee-run "Business Resource Group" communities to world-class benefits like parental leave, 401k and privileges like space available travel, United is truly a one-of-a-kind place to work. Are you ready to travel the world? We believe that inclusion propels innovation and is the foundation of all that we do. United's Digital Technology team spans the globe and is made up of diverse individuals all working together with cutting-edge technology to build the best airline in the history of aviation. Our team designs, develops and maintains massively scaling technology solutions brought to life with innovative architectures, data analytics, and digital solutions.

Responsibilities

• Collaborate with ML engineers, data scientists, and data engineers to support Machine Learning needs for commercial and operational projects.
• Design, architect, implement, and lead key components of the Machine Learning Platform and Gen AI/ML business use cases.
• Establish processes and best practices for machine learning projects.
• Build high-performance, cloud-native machine learning infrastructure and services to enable rapid innovation across United.
• Set up containers and Serverless platform with cloud infrastructure.
• Design and develop tools and apps to enable ML automation using AWS ecosystem.
• Build data pipelines to enable ML models for batch and real-time data.
• Support large scale model training and serving pipelines in a distributed and scalable environment.
• Stay aligned with the latest developments in cloud-native and ML ops/engineering and experiment with new technologies.
• Optimize, fine-tune generative AI/LLM models to improve performance and accuracy and deploy them.
• Evaluate the performance of LLM models and implement LLMOps processes to manage the end-to-end lifecycle of large language models.

Requirements

• Bachelor's degree in Computer Science, Data Science, Generative AI, Engineering or related discipline or Mathematics experience.
• 7+ years of software engineering experience with languages such as Python, Go, Java, Scala, Kotlin, or C/C++.
• 7+ years of experience in machine learning, deep learning, and natural language processing.
• Strong technical leadership and familiarity with data science methodologies and frameworks (e.g., PyTorch, Tensorflow) and preferably building and deploying production ML pipelines.
• Experience in ML model life cycle development and familiarity with common algorithms like XGBoost, CatBoost, Deep Learning, etc.
• Experience setting up and optimizing data stores (RDBMS/NoSQL) for production use in the ML app context.
• Cloud-native DevOps, CI/CD experience using tools such as Jenkins or AWS CodePipeline; experience with GitOps using tools such as ArgoCD, Flux, or Jenkins X.
• Experience with generative models such as GANs, VAEs, and autoregressive models.
• Prompt engineering: Ability to design and craft prompts that evoke desired responses from LLMs.
• LLM evaluation: Ability to evaluate the performance of LLMs on a variety of tasks, including accuracy, fluency, creativity, and diversity.
• LLM debugging: Ability to identify and fix errors in LLMs, such as bias, factual errors, and logical inconsistencies.
• LLM deployment: Ability to deploy LLMs in production environments and ensure that they are reliable and secure.
• Experience with LLMOps (Large Language Model Operations) to manage the end-to-end lifecycle of large language models.
• Experience with generative AI methods such as retrieval augmented generation (RAG) and instruction fine tuning.
• Must be legally authorized to work in the United States for any employer without sponsorship.

Nice-to-haves

• Master's/PhD degree in Computer Science or related STEM field.
• 5+ years of experience working in cloud environments (AWS preferred) - Kubernetes, Dockers, ECS and EKS.
• 5+ years of experience with Big Data technologies such as Spark, Flink and SQL programming.
• 5+ years of experience with cloud-native DevOps, CI/CD.
• 3-5+ years of relevant enterprise Architecture experience.

Benefits

• Parental leave
• 401k
• Space available travel privileges