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United Airlines

Flies passengers and cargo worldwide from US hubs

AI Engineering Senior Manager - AI Platform Engineering

Engineering LeadershipConfirmed live in the last 24 hours
Full-timeChicago, IL, USAIn-person

About the Job

Requirements

  • A bachelor's degree in Computer Science, Data Science, Engineering, or a related field is required.
  • At least 5 years of experience leading product or technical teams and delivering large-scale initiatives is required.
  • At least 2 years of Generative AI experience is required.
  • Experience guiding cross-functional teams through complex, multi-stakeholder programs is required.
  • Candidates must be legally authorized to work in the United States for any employer without sponsorship.
  • Successful completion of an interview is required to meet the job qualification.
  • Reliable, punctual attendance is an essential function of the position.

Responsibilities

  • Drive the architecture, development, and operations of machine learning engineering and Generative AI systems, enabling scalable and responsible AI solutions across the business.
  • Define and execute the machine learning and Generative AI platform strategy aligned with enterprise digital transformation objectives.
  • Lead work involving Generative AI and AI agents.
  • Own the platform roadmap, architecture decisions, and budget planning to scale AI capabilities across the enterprise.
  • Collaborate with the Chief Digital Officer, Chief Information Officer, and senior stakeholders to identify, prioritize, and fund impactful AI and Generative AI investments.
  • Represent the Machine Learning Center of Excellence in cross-functional meetings and strategic planning forums.
  • Communicate strategy, progress, and outcomes to executive stakeholders through presentations and business narratives.
  • Serve as the primary liaison between the Center of Excellence and business units, communicating technical capabilities and business impact.
  • Lead initiatives involving large language models and foundation models, including OpenAI, Anthropic, and Hugging Face.
  • Design and operationalize Generative AI pipelines, including retrieval-augmented generation, prompt orchestration, fine-tuning, and safety guardrails.
  • Work with AI engineers to drive architecture, design, and implementation of key components of the agentic AI and machine learning platform.
  • Build and deploy secure, scalable Generative AI applications with an emphasis on privacy, safety, and compliance.
  • Integrate large language models into enterprise workflows, including copilots, document summarization, intelligent assistants, and domain-specific question-and-answer systems.
  • Partner with business leaders to identify opportunities where agentic AI and machine learning can create measurable value, and translate business needs into solution designs with guardrails, validation approaches, and measurable success metrics.
  • Design, manage, and monitor the enterprise AI engineering platform to ensure scalability, reliability, and automation.
  • Own observability and resilient architecture.
  • Develop AIOps processes to monitor model performance, detect drift, and automate retraining and validation.
  • Build and maintain tools and frameworks to govern Generative AI models for compliance, bias, versioning, traceability, and auditability.
  • Design and implement feature engineering and data pipelines to deliver high-quality training data and inference-ready datasets.
  • Partner with data scientists and engineers to create reusable, production-grade feature stores and pipelines.
  • Solve data ingestion, transformation, and governance challenges in collaboration with data platform and DataOps teams.
  • Develop integrated machine learning and AI solutions on enterprise analytics platforms.
  • Hire, mentor, and grow a high-performing AI engineering team focused on innovation, execution, and impact.
  • Provide technical mentorship and guidance to AI engineers and data scientists, ensuring high standards in design and implementation.
  • Promote a culture of continuous learning, experimentation, and operational excellence.

Desired Qualifications

  • A master's degree is preferred.
  • At least 10 years of experience delivering large-scale initiatives is preferred.
  • Experience with MLflow, KServe, SageMaker, Vertex AI, Databricks, Hugging Face, LangChain, agentic frameworks, agent evaluations, Spark, Kafka, Delta Lake, Kubernetes, Jenkins, GitOps, and Terraform is desired.

About the Company

United Airlines

Flies passengers and cargo worldwide from US hubs

United Airlines flies passengers and cargo across the United States and to destinations around the world, running a hub network that spans Chicago, Denver, Houston, Los Angeles, Newark, San Francisco, Washington Dulles and Guam. It was a founding member of Star Alliance, the global airline partnership that links its routes and frequent-flyer benefits with carriers such as Lufthansa and Air Canada. Travelers book seats in economy, premium and Polaris business cabins, and United earns money from fares, bag and seat fees, cargo, and its MileagePlus loyalty program, which sells miles to card partners. United traces its roots to Varney Air Lines, which began flying airmail in 1926, and is based in Chicago.

Company size
10,001+
Company stage
IPO
Headquarters
Chicago, IL
Founded
1926

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