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AI Platform Site Reliability Engineering Specialist

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NTT DATA

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Location:
India , Bengaluru

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Category:
IT - Software Development

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Contract Type:
Not provided

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Salary:

Not provided
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Job Description:

The AI Platform Site Reliability Engineering Specialist will operate and maintain the infrastructure for GenAI applications, focusing on automation and reliability.

Job Responsibility:

  • Operate, monitor, and maintain the infrastructure supporting GenAI applications ( training, inference, feature store, data ingestion, model serving)
  • Design and build automation for core platform capabilities, reducing manual toil
  • Develop and maintain infrastructure-as-code (IaC) for provisioning and managing compute, storage, network, GPU clusters, Kubernetes / container orchestration, etc.
  • Establish, monitor and enforce SLOs/SLIs/LSAs, error budgets, alerting, and dashboards
  • Lead incident response, root cause analysis (RCA), postmortems, and systemic remediation
  • Perform capacity planning, scaling strategies, workload scheduling and resource forecasting
  • Optimize cost vs. performance trade-offs in large-scale compute environments
  • Harden systems for security, compliance, auditability, and data governance
  • Collaborate across teams (cloud engineers, data engineers, infrastructure, security) to ensure safe deployment, rollout, rollback, and integration of new systems
  • Define disaster recover (DR) strategies, back/restore practices, fault tolerance mechanisms
  • Maintain runbooks, operation playbooks, documentation, and training materials
  • Participate in on-call rotations and respond to production incidents 24/7 as needed
  • Continuously evaluate and integrate new tools, frameworks, or technologies to enhance platform reliability

Requirements:

  • Bachelor's or Master's degree in Computer Science or related field, or equivalent job experience
  • 5 years of production experience in SRE / Infrastructure / ops for large-scale systems
  • Strong programming/scripting skills (Python, Go, Java, or equivalent)
  • Deep experience with containerization (Docker), orchestration (Kubernetes, etc.)
  • Infrastructure-as-code (Terraform, Helm, CloudFormation, Ansible, etc.)
  • Familiarity with GPU / AI compute clusters, high-performance data storage, and distributed architectures
  • Experience with monitoring / observability / logging / alerting tools (Prometheus, Grafana, ELK / EFK, Datadog, etc.)
  • Networking and systems engineering knowledge (TCP/IP, DNS, routing, load balancing, distributed storage)
  • Solid experience in capacity planning, performance tuning, scaling, and incident response
  • Demonstrated ability to lead RCAs, deploy fixes, and drive reliability improvements
  • Experience in regulated environments (financial services, compliance, audit, security) is a strong plus
  • Excellent communication, documentation, and cross-team collaboration skills
  • Proven track record of reducing operational toil via automation

Nice to have:

  • Understanding of SRE techniques
  • Proficiency with Open Telemetry tools including Grafana, Loki, Prometheus, and Cortex
  • Good knowledge of Microservice based architecture, industry standards, for both public and private cloud
  • Knowledge of data pipeline technologies (Kafka, Spark, Flink, etc.)
  • Good knowledge of various DB engines (SQL, Redis, Kafka, Snowflake, etc.) for cloud app storage
  • Experience working with Generative AI development, embeddings, fine tuning of Generative AI models
  • Experience in high-performance computing (HPC), distributed GPU cluster scheduling (e.g. Slurm, Kubernetes GPU scheduling)
  • Understanding of ModelOps / ML Ops / LLM Op
  • Experience with chaos engineering, canary deployments, blue/green rollouts

Additional Information:

Job Posted:
February 14, 2026

Work Type:
On-site work
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