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Tier-1 brand attracts many applicants despite specialized AI-infrastructure skill requirements.
Requires niche AI-platform and MLOps experience, limiting cross-industry transferability.
Many mandatory specialized MLOps, Kubernetes, and cloud infrastructure skills required.
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Design, build, and operate enterprise AI platforms for deploying, scaling, and managing large language model (LLM)-powered applications and AI agents.
Develop systems for autonomous operations including incident response, root cause analysis, infrastructure provisioning, remediation, and AI-assisted change management.
Build observability and assessment frameworks to monitor AI model performance, latency, cost, safety, and operational workflows on a large scale.
Significant experience with AI platform engineering and MLOps, including deploying and managing LLM workloads on Kubernetes and GPU infrastructure.
Strong programming skills in Python and Golang with experience in building distributed systems and APIs.
Proficiency with Kubernetes, AWS, Terraform, Docker, Helm, ArgoCD, GitOps and cloud platform engineering.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced engineer with a strong background in AI-native application development rather than only using AI APIs, familiar with agentic AI, prompt engineering, and AI evaluation frameworks.
Proficient in building scalable and secure AI model serving platforms, MLOps pipelines, and autonomous operational tooling.
Familiar with observability tools and techniques for AI systems (e.g., Prometheus, Grafana, OpenTelemetry) and have worked on AI-assisted developer productivity and autonomous remediation systems.