Member of Technical Staff - Platform Engineering

Bangalore, India

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Pay
Not stated by the employer
Work mode
Not stated
Location
Bangalore, India
Posted
Oct 18, 2026Greenhouse first-published date
Last checked
Oct 18, 2026

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Where this job mentions Go

Build Agentic & Go-Based Automation: Develop robust, type-safe internal tooling and AI agents in Go and Python to automate root-cause analysis, disaster recovery, and proactive infrastructure optimization.

The posting lists Go among its requirements.

Also mentioned

PythonKubernetesDockerKafkaRedis

Employer posting

Read the full description on job-boards.greenhouse.io

Source details
Source publication
Source first published: Oct 18, 2026 · 9:53 PM PDT
Date provenance
Greenhouse first_published · source field, not independently verified
ATS updated (not posted)
Oct 18, 2026 · 9:54 PM PDT
First imported
Oct 18, 2026 · 10:07 PM PDT
Last checked at source
Oct 18, 2026 · 10:07 PM PDT
Last seen at source
Oct 18, 2026 · 10:07 PM PDT
Snapshot built
Oct 18, 2026 · 10:08 PM PDT
Source status at collection
open
Board
greenhouse:purestorage
Source ID
greenhouse:purestorage:8276105
Evidence field
description
Match rule
requirements_context · 3 Go match(es)
Department as supplied
Engineering

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Location & work details

Office location and remote eligibility are separate. A country flag describes the stated location, not who may apply.

Original location
Bangalore, India
Country
Bangalore, India (ATS location fields)
Stack
Build Agentic & Go-Based Automation: Develop robust, type-safe internal tooling and AI agents in Go and Python to automate root-cause analysis, disaster recovery, and proactive infrastructure optimization. (Source language/tool mention)
Stack
Build Agentic & Go-Based Automation: Develop robust, type-safe internal tooling and AI agents in Go and Python to automate root-cause analysis, disaster recovery, and proactive infrastructure optimization. (Source language/tool mention)
Stack
Ensure Reproducible Deployments: Maintain "Golden Path" deployment standards using Docker and Kubernetes to keep models, prompts, and application code perfectly synchronized across all environments. (Source language/tool mention)
Stack
Ensure Reproducible Deployments: Maintain "Golden Path" deployment standards using Docker and Kubernetes to keep models, prompts, and application code perfectly synchronized across all environments. (Source language/tool mention)
Stack
Scale Event-Driven AI Pipelines: Architect asynchronous processing systems using Kafka and RabbitMQ to trigger dynamic infrastructure scaling and handle high-volume data streams like long-running document ingestion. (Source language/tool mention)
Stack
Optimize AI Observability & Performance: Manage GPU workloads, distributed caches (Redis), and vector databases while monitoring system health and AI-specific metrics like token latency and model drift. (Source language/tool mention)
Normalization
Explicit source location text; no city-only country inference.

No application or candidate data is collected here. The employer controls current availability and eligibility.