Senior ML Platform Architect - Cloud-Native GenAI

United Airlines; Chicago (IL); On-site; USD 147,000 - 192,000; Full time

Benefits offered by this job

Flight privileges

Benefits package

Job summary

United Airlines is seeking a talented Data and Machine Learning Engineer to join the Digital Technology team. You will design, build, and scale ML platforms and Gen AI/LLM solutions, collaborating with ML engineers, data scientists, and data engineers to deliver production-ready pipelines.

You’ll work on cloud-native infrastructure, data pipelines, model training and deployment, and ML ops practices, with exposure to PyTorch, TensorFlow, Spark, and Flink.

Qualifications

• Bachelor's degree in Computer Science, Data Science, Generative AI, Engineering or related discipline or Mathematics
• 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

Responsibilities

• 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
• 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

Skills

Python

Go

Java

C/C++

Machine Learning

Deep Learning

NLP

PyTorch

TensorFlow

ML pipelines

AWS

Kubernetes

Docker

ECS/EKS

GitOps

ArgoCD

Spark

Flink

Education

Bachelor's degree in Computer Science, Data Science, Generative AI, Engineering or related discipline or Mathematics

Tools

Jenkins

AWS

Kubernetes

Docker

ECS

EKS

GitOps

ArgoCD

Flux

Jenkins X

Spark

Flink

Job description

United Airlines is seeking a talented Data and Machine Learning Engineer to join the Digital Technology team. You will design, build, and scale ML platforms and Gen AI/LLM solutions, collaborating with ML engineers, data scientists, and data engineers to deliver production-ready pipelines.

You’ll work on cloud-native infrastructure, data pipelines, model training and deployment, and ML ops practices, with exposure to PyTorch, TensorFlow, Spark, and Flink.