Job Overview
We are seeking a Senior AI Platform Engineer to build and operate the production AI platform that powers ClusterWareAI. In this role, you will develop the infrastructure, services, and tooling that enable our AI Operational Agent to reliably deploy, retrieve knowledge, execute workflows, and operate at scale in enterprise environments.
You will work closely with AI engineers, platform engineers, and software developers to build secure, scalable, observable, and highly available AI services for production deployments.
Responsibilities
Build and operate the production AI platform that supports model serving, inference, and AI services.
Develop scalable retrieval, knowledge management, and data ingestion pipelines to support AI-driven operations.
Build APIs, platform services, and automation that integrate AI capabilities into ClusterWareAI.
Implement AI observability, monitoring, evaluation, and operational tooling for production AI services.
Optimize inference performance, scalability, reliability, and operational efficiency.
Build and maintain CI/CD pipelines and deployment automation for AI applications.
Partner with AI, platform, and software engineering teams to deploy AI capabilities into production.
Contribute to platform security, reliability, and operational excellence.
Qualifications
Bachelor's degree in Computer Science, Engineering, or a related field.
7+ years of software engineering, platform engineering, or infrastructure engineering experience.
Strong programming skills in Python.
Experience developing and operating production AI or LLM-powered applications.
Experience with Kubernetes, Docker, and cloud-native architectures.
Experience with model serving technologies such as vLLM, NVIDIA Triton Inference Server, or similar platforms.
Experience building REST APIs, microservices, and distributed systems.
Familiarity with vector databases, retrieval systems, and RAG architectures.
Preferred Qualifications
Experience implementing monitoring, observability, and operational tooling for AI services.
Experience with managed AI platforms such as Azure AI Foundry, AWS Bedrock, or Google Vertex AI.
Experience with AI engineering tools such as LangSmith, MLflow, OpenTelemetry, or similar technologies.
Experience deploying GPU-based AI workloads in Kubernetes environments.
Experience integrating AI services with enterprise applications, APIs, or infrastructure platforms.
Background in enterprise infrastructure, cloud platforms, or systems management software.
Location
Hybrid - Bangalore
Travel
As needed
Apply now »
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