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Deutsche Bank

Finances businesses and manages personal wealth

Aiops Engineer - Nct

Site Reliability Engineering +1Updated on Nov 12
Full-timePune, Maharashtra, IndiaNo salary listedIn-person

About the Job

Requirements

  • Hands-on experience in cloud engineering, DevOps, Site Reliability Engineering, Machine Learning Operations, or production engineering, with practical ownership of automation and troubleshooting tasks.
  • Practical Google Cloud Platform knowledge, including Google Kubernetes Engine/Kubernetes, Cloud Run, Identity and Access Management, Virtual Private Cloud networking, Cloud Storage, Cloud Logging, and Cloud Monitoring.
  • Experience with Terraform, Git, GitHub Actions or equivalent continuous integration and delivery tooling, Docker, and secure software delivery practices.
  • Strong Python programming skills, with working knowledge of shell scripting, Structured Query Language, Representational State Transfer APIs, and Linux troubleshooting.
  • Experience implementing observability, analysing logs and metrics, investigating incidents, and creating operational dashboards and runbooks.
  • Working knowledge of Artificial Intelligence/Machine Learning and Generative AI applications, including model APIs, prompt and inference patterns, retrieval-augmented generation, agent workflows, and evaluation.
  • Understanding of least privilege, workload identity, secrets management, network security, vulnerability management, and auditable change practices.
  • Understanding of responsible AI, data protection, traceability, and human oversight, with the ability to translate control requirements into practical engineering actions.
  • Proven ability to leverage AI tools to enhance productivity and optimise workflows to solve business problems, while applying critical judgment to ensure responsible and ethical use of data and AI outputs.

Responsibilities

  • Build and maintain reusable AIOps capabilities for telemetry collection, alert correlation, incident context enrichment, root-cause analysis, and operational automation.
  • Engineer and support cloud-native AI services on Google Cloud Platform, using Google Kubernetes Engine, Cloud Run, Vertex AI, storage, messaging, and approved enterprise integrations within agreed ownership boundaries.
  • Develop continuous integration and delivery pipelines and reusable infrastructure-as-code modules using GitHub Actions, Terraform, and container tooling, with testing, security checks, and controlled releases.
  • Implement logs, metrics, traces, dashboards, health checks, and service-level indicators to improve service visibility and reduce alert noise.
  • Develop AI-assisted diagnostic and runbook workflows, integrating operational knowledge and incident history; evaluate output accuracy and retain human review for consequential decisions.
  • Implement policy-bound remediation through approved pipelines, with least-privilege identities, approval gates, rollback, failure safeguards, and traceable action records.
  • Contribute to production incident investigation and engineering fixes; enable L1/L2 support through runbooks, diagnostic guidance, knowledge transfer, and clear escalation paths.
  • Improve production readiness through capacity and performance analysis, recovery testing, monitoring, support handovers, and release/change documentation.
  • Embed applicable security, data protection, and AI controls into engineering workflows; support vulnerability remediation and maintain evidence with designated control owners.
  • Automate onboarding, service-management integration, and routine operational tasks; measure improvements in detection and resolution time, manual effort, service reliability, and cost.
  • Document reusable patterns and contribute to proofs of concept, technical reviews, and continuous improvement with global engineering and support teams.

Desired Qualifications

  • Experience with Vertex AI, OpenTelemetry, ServiceNow integrations, agent frameworks, model/agent evaluation, or controlled remediation is an advantage.
  • A bachelor's or master's degree in computer science, engineering, or an equivalent discipline is desirable.
  • Relevant Google Cloud, Kubernetes, DevOps/Site Reliability Engineering, or Artificial Intelligence/Machine Learning certifications and experience in banking or another regulated industry are advantageous.

About the Company

Deutsche Bank

Finances businesses and manages personal wealth

Deutsche Bank finances companies, governments and individuals through a global banking network rooted in Germany and spanning major financial markets. Its investment bank focuses on bonds and currencies, alongside advice on mergers and raising capital, while its private bank handles household banking and wealth management. Businesses use its cash management, trade finance and lending services to move money and fund operations, and the group manages investment funds through DWS. It earns money from loan interest, transaction and investment fees, and trading activity. Deutsche Bank traces its founding to 1870 and is headquartered in Frankfurt am Main, Germany.

Company size
10,001+
Company stage
IPO
Headquarters
Frankfurt, Germany
Founded
1870

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