Senior Engineer - Data Operations

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 whatu2019s next. Letu2019s 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

This role exists to design, automate, and operate secure, scalable cloud and DevOps platforms that support Unitedu2019s Data Engineering Operations capabilities. The position is responsible for enabling reliable application delivery through CI/CD, infrastructure as code, cloud platform management, observability, and operational automation. It also advances AI-enabled DevOps practices by applying AIOps, generative AI, intelligent automation, and responsible guardrails to improve engineering productivity, system reliability, incident response, and operational efficiency.

• Design, deploy, and maintain secure, scalable AWS cloud infrastructure, Kubernetes platforms, Terraform modules, and core networking/services to support reliable data engineering operations.

• Implement, manage, and optimize CI/CD pipelines, Infrastructure as Code, deployment automation, security validation, automated testing, and release reliability practices.
• Operate Databricks, AI/ML, and generative AI workloads through platform administration, environment provisioning, deployment automation, monitoring, rollback, MLOps, and LLMOps practices.

• Implement monitoring, observability, AIOps, anomaly detection, event correlation, alerting, incident triage, log analysis, root-cause analysis, and remediation recommendations.

• Build automation using Python, Bash, APIs, and event-driven services to reduce manual effort, improve operational efficiency, and strengthen platform reliability.

• Apply cloud security, DevSecOps, responsible AI, governance controls, human approvals, auditability, and documentation practices across AI-enabled operational workflows.

This position is offered on local terms and conditions. Expatriate assignments and sponsorship for employment visas, even on a time-limited visa status, will not be awarded. This position is for United Airlines Business Services Pvt. Ltd - a wholly owned subsidiary of United Airlines Inc.

Qualifications

Whatu2019s needed to succeed (Minimum Qualifications):

• Bacheloru2019s degree required.
• Computer Science, Engineering, Information Technology, or related field.
• 5+ years of IT experience in DevOps, Cloud Engineering, SRE, Platform Engineering, or similar roles with AWS, CI/CD, IaC, scripting, containers, databases, and operational support.
• Must have AWS infrastructure, Terraform, Kubernetes, CI/CD, Python/Bash, monitoring, cloud security, database, troubleshooting, communication, AI-assisted engineering, generative AI fundamentals, and responsible AI evaluation skills.
• Must be legally authorized to work in India for any employer without sponsorship
• Must be fluent in English (written and spoken)
• 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):

• Masteru2019s in Computer Science, Engineering, Artificial Intelligence, Data Science, or related STEM field.
• Experience in airline/transportation DevOps, AIOps, generative AI engineering workflows, AI agents, MLOps/LLMOps, Databricks, observability, Harness, microservices, serverless, and DevSecOps environments.
• AWS, Databricks, Kubernetes, Terraform, DevOps, AI/ML, or cloud security certifications preferred.

• Preferred knowledge includes AIOps, prompt engineering, LLM APIs, retrieval-augmented generation, AI observability, model monitoring, deployment-risk assessment, cost optimization, human-in-the-loop controls, and AI governance.