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Principal Architect

2 months ago


Chicago, Illinois, United States United Airlines Full time
About the Role

We are seeking a highly skilled and experienced Principal Architect to join our Data and Machine Learning Engineering team at United Airlines. As a key member of our team, you will be responsible for designing, developing, and leading key components of our Machine Learning Platform, as well as establishing processes and best practices.

Key Responsibilities:
  • Design and develop high-performance, cloud-native machine learning infrastructure and services to enable rapid innovation across the organization.
  • 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.
  • 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 environments.
  • 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.
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.
  • 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; 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.
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.