Member of Technical Staff- AIops
Pure StorageBangalore, India
Full Time
Posted February 24, 2026
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Job Description
A Software Engineer within the AI Applications team responsible for designing and building scalable, reliable AI infrastructure, pipelines, and automation tools to support high-velocity AI experimentation and deployment in a cloud environment.
Key Responsibilities
- Architect platform self-service capabilities with internal APIs and abstractions for AI environment provisioning
- Develop internal tools and AI agents to automate infrastructure root-cause analysis and system optimization
- Design and implement asynchronous, event-driven AI data pipelines using Kafka or RabbitMQ
- Standardize AI deployment processes using Docker and Kubernetes to ensure reproducibility across environments
- Implement advanced monitoring and observability for AI-specific metrics to ensure system reliability under heavy loads
Requirements
- Extensive experience in Platform Engineering with a focus on writing clean, testable Go and Python code to manage complex cloud environments and GPU workloads.
- Practical experience integrating LLM APIs, managing Vector Databases, and optimizing Retrieval-Augmented Generation (RAG) pipelines and distributed caches such as Redis.
- Deep understanding of Go's goroutines and channels to handle the massive data throughput required by Kafka-driven AI pipelines.
- Proven ability to manage Kubernetes (K8s) at scale, specifically extending the K8s API with custom controllers to make clusters AI-aware and managing specialized NVMe storage.
- Sophisticated knowledge of asynchronous systems, knowing when to leverage Kafka for streaming versus RabbitMQ for complex task routing.
- Ability to create internal APIs and Go-based abstractions that enable engineers to provision AI-ready environments including model weights, vector databases, and event streams with a single command.
- Experience developing internal tools and AI Agents using Go and Python to automate root-cause analysis of infrastructure failures and proactively optimize system performance.
- Experience designing and implementing asynchronous processing pipelines using Kafka or RabbitMQ to manage high-volume data ingestion for RAG systems and real-time model demand.
- Experience standardizing AI deployments using Docker and Kubernetes to ensure models, prompts, and code are perfectly synchronized across all environments.
- Implementing advanced monitoring in Prometheus and Grafana to track AI-specific metrics such as token latency and model drift, ensuring zero-downtime reliability under heavy inference loads.
- Willingness to work from the Bengaluru office in accordance with company policies, unless on PTO, work travel, or other approved leave.
Benefits & Perks
Competitive salary range (not specified)
Work from the Bengaluru office in compliance with company policies
Flexible time off
Wellness resources
Company-sponsored team events
Support for growth and development
Inclusive and diverse work environment
Ready to Apply?
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