14mo ago

Cloud Infrastructure Engineer

India·BangaloreFull Timemid

EngineeringOtherDevOps & InfrastructureCloud Infrastructure EngineerDevops EngineerInfrastructure & CloudGoogle Cloud Infrastructure Engineer

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Quick Summary

Overview

About zaimler zaimler is building the next-generation semantic platform that links fragmented enterprise data and extracts meaning with knowledge-distilled models. We’re creating the foundation for AI systems that don’t just generate, but retrieve, link, and reason over enterprise knowledge.

Technical Tools

awsazuregcpgokafkakubernetespythonterraformdistributed-systems

About zaimler

zaimler is building the next-generation semantic platform that links fragmented enterprise data and extracts meaning with knowledge-distilled models. We’re creating the foundation for AI systems that don’t just generate, but retrieve, link, and reason over enterprise knowledge.

In just over a year, we’ve begun partnering with Fortune 500 design partners in insurance, travel, and technology, deploying our semantic platform into some of the world’s most complex and high-volume data ecosystems. Our platform enables enterprises to make their data AI-ready from the start: automating ontology creation, data mapping, and retrieval-augmented reasoning at scale.

Our team comes from LinkedIn, Visa, Meta, and Branch, and has spent decades solving data and infrastructure challenges at scale. Backed by top VCs, we’re building the next foundational layer for enterprise AI.

What You’ll Do

As our Founding Cloud Infrastructure Engineer, and an early hire in our Bangalore office, you’ll build the foundation of zaimler’s cloud infrastructure while shaping our presence in India. You’ll also play a key role in supporting our customers across the Asia-Pacific region, ensuring the systems you design scale globally and deliver real impact locally

Architect & Scale Infra: Design and deploy secure, fault-tolerant cloud infrastructure across AWS/Azure/GCP, using Kubernetes, Terraform, and modern IaC tools.

Enable Distributed AI Workloads: Build and optimize compute systems for distributed frameworks like Ray and Kafka, ensuring scalability and reliability.

GPU Orchestration: Manage GPU resources and scheduling for ML inference, retrieval, and training workloads.

Drive Observability & Reliability: Implement monitoring, security, and best practices for high-availability ML/AI systems.

Collaborate Across Teams: Work hand-in-hand with ML researchers and data scientists to ensure infra accelerates, not hinders, development.

We’re open to multiple levels of seniority (3–10+ years of engineering experience). What matters most is founder mindset + strong infra chops.

Hands-on with Kubernetes + Terraform (real-world deployments, not just exposure).

Experience with distributed systems (Ray, Dask, Spark, or similar).

Exposure to Kafka or equivalent MQ systems.

Strong programming/scripting in Python, Go, or similar.

Track record of building or operating production-grade infra.

GPU scheduling/optimization experience.

Prior startup/early-stage build-from-scratch experience.

Experience supporting ML/AI workloads in production.