Principal Architect - Machine Learning

Principal Architect - Machine Learning

Location Chicago, Illinois, United States This job is associated with 2 categories See allJob IdWHQ00026406Information TechnologyJob TypeFull-TimePosted Date 08/17/2026

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Achieving our goals starts with supporting yours. Grow your career, access top-tier health and wellness benefits, build lasting connections with your team and our customers, and travel the world using our extensive route network.

Come join us to create what’s next. Let’s define tomorrow, together.

Description

United's Digital Technology team is comprised of many talented 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.

Job overview and responsibilities

United Airlines is seeking talented people to join the Data and Machine Learning Engineering team. The organization is responsible for leading data driven insights & innovation to support the Machine Learning needs for commercial and operational projects with a digital focus. This role will frequently collaborate with ML engineers, data scientists and data engineers. This role will design, architect, implement and lead key components of the Machine Learning Platform, Gen AI/ML business use cases, and establish processes and best practices.

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

You will 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

Hands on development expertise of Spark and Flink for both real time and batch applications

Support large scale model training and serving pipelines in distributed and scalable environment

Stay aligned with the latest developments in cloud-native and ML ops/engineering and to experiment with and learn new technologies – NumPy, data science packages like sci-kit, microservices architecture

Optimize, fine-tune generative AI/LLM models to improve performance and accuracy and deploy them

Evaluate the performance of LLM models, Implement LLMOps processes to manage the end-to-end lifecycle of large language models

Develop, optimize, fine-tune Generative AI/LLM models to improve performance and accuracy and deploy them

Qualifications

What’s needed to succeed (Minimum Qualifications):

Bachelor's degree in

Computer Science, Data Science, Generative AI, Engineering or related discipline or Mathematics experience required

5+ years of software engineering experience with languages such as Python, Go, Java, or C/C++

5+ years of experience in machine learning, deep learning, and natural language processing

Strong software engineering experience with Python and at least one additional language such as Go, Java, or C/C++

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 experience and prefer experience to 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; preferably 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) or AgenticOps (Agentic 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

Successful completion of interview required to meet job qualification

Reliable, punctual attendance is an essential function of the position

What will help you propel from the pack (Preferred Qualifications):

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

1+ years of experience with Generative AI/LLMs

The base pay range for this role is $147,060.00 to $191,516.00.

The base salary range/hourly rate listed is dependent on job-related, factors such as experience, education, and skills. This position is also eligible for bonus and/or long-term incentive compensation awards.

• Bachelor's degree in Computer Science, Data Science, Generative AI, Engineering, or related field with 5+ years software engineering experience in Python and another language.
• 5+ years experience in machine learning, deep learning, NLP, and ML model lifecycle development with frameworks like PyTorch or TensorFlow.
• Experience with cloud-native DevOps/CI/CD, ML infrastructure, generative models, LLMOps, and legal authorization to work in the U.S. without sponsorship.